Do leagues generally differ between themselves on the pitch by different game aspects and how?

Have you ever wondered do leagues between themselves differ between each other based on the overall performance on the pitch? I mean of course they differ, if you have team with 100 times more values than team in another league, but how do they differ? And how would you identify the differences between leagues?

I have tested nine leagues in last season (2020/2021) by different aspects of the game: England, Spain, German, Italy, France, Belgium, Austria, Croatia and Poland. And I have used four criteriums to identify those differences:

1. What are key factors to create a chance in the individual league?

2. What are key factors on the pitch for scoring a goal in the individual league?

3. What are key factors on the pitch for not losing the game in the individual league?

4. What are key factors on the pitch for winning a match in individual league?

To identify those goals, I have used:

1. frequency (what is most used in each league),
2. statistical correlation and
3. statistical regression.

The frequency of 104 observed aspects of the game will tell us which aspect of the match is happening the most. That will not help us understanding how to reach each of criterium.

On the opposite, statistical correlation will tell us what correlate with each of criterium and help us started understanding each of criteriums per country. So, more interesting will be those aspects of the game which have stronger correlation with each of criterium.

Regression models will indicate us what is specific for reaching each of criteriums per each country and help us better understand each of criteriums. For best understanding for each country will be summarized correlations and regression models as preparation for interpretation.

For all observed dependent variables (criteriums) aim was to get the highest possible R number with the same methodology for all observed dependent variables and for all leagues. R number explains the power of the model. It lies between 0 and 1. Higher the number more precise model. It actually explains the number of variations in the dependent variable which are explained with the independent variables. Adjusted R square is R number multiplied with adjustment factor created by comparison different regression models with different independent variables.

All variables in model are statistically significant, and those which are positively statistically significant in Pearson correlation are marked green and those which are negatively statistically significant in Pearson correlation are marked red. Those which are not statistically significant in Pearson correlation are not marked. Mostly VIF number is below 5, almost all bellow 10, and rarely above 10.

Finally, I will leave each of you to give “sweet” interpretation of results. And would like to hear your thoughts about interpretation from your side 😉

In each of following heatmap and highlight map correlation of different aspects of game are presented in tables. So, let’s start with correlations between “creating a chance” and different aspects of game per countries.

Table 1.: Heatmap of correlations between chances and different aspects of game per countries

CHANCES	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.178	0.158	0.160	0.078	0.197	0.199	0.098	0.252	0.145
Attacks with shots – left flank	0.413	0.400	0.350	0.364	0.465	0.386	0.414	0.421	0.485
Efficiency for attacks through the left flank, %	0.361	0.374	0.289	0.353	0.415	0.363	0.398	0.316	0.456
Attacks – center	0.201	0.121	0.233	0.257	0.111	0.264	0.196	0.088	0.108
Attacks with shots – center	0.438	0.343	0.423	0.422	0.383	0.489	0.380	0.240	0.370
Efficiency for attacks through the central zone, %	0.373	0.300	0.348	0.333	0.360	0.397	0.342	0.222	0.332
Attacks – right flank	0.267	0.118	0.069	0.122	0.131	0.192	0.178	0.278	0.113
Attacks with shots – right flank	0.450	0.390	0.323	0.392	0.411	0.421	0.450	0.427	0.389
Efficiency for attacks through the right flank, %	0.380	0.354	0.321	0.375	0.377	0.414	0.399	0.338	0.386
Positional attacks	0.354	0.240	0.229	0.235	0.213	0.311	0.229	0.324	0.176
Positional attacks with shots	0.579	0.547	0.512	0.547	0.532	0.558	0.568	0.505	0.537
% of efficiency for positional attacks	0.538	0.516	0.479	0.532	0.504	0.536	0.543	0.428	0.527
Counter-attacks	0.122	0.032	0.085	0.099	0.169	0.188	0.179	0.159	0.159
Counter-attacks with a shot	0.375	0.307	0.319	0.342	0.420	0.384	0.371	0.332	0.439
% of efficiency for counterattacks	0.354	0.304	0.307	0.333	0.367	0.309	0.314	0.293	0.401
Set pieces attacks	0.296	0.199	0.329	0.154	0.317	0.247	0.209	0.408	0.258
Attacks with shots – Set pieces attacks	0.429	0.327	0.381	0.384	0.448	0.393	0.359	0.496	0.397
% of efficiency for set-piece attacks	0.292	0.261	0.167	0.317	0.293	0.287	0.253	0.282	0.280
Free-kick attacks	-0.022	-0.054	0.073	-0.036	0.032	-0.039	-0.008	0.129	0.010
Free-kick attacks with shots	0.170	0.111	0.170	0.170	0.203	0.147	0.137	0.231	0.182
% of efficiency for free-kick attacks	0.192	0.166	0.148	0.214	0.201	0.155	0.151	0.187	0.232
Corner attacks	0.420	0.338	0.339	0.359	0.420	0.404	0.359	0.459	0.368
Corner attacks with shots	0.411	0.319	0.319	0.363	0.419	0.372	0.358	0.449	0.364
% of efficiency for corner attacks	0.162	0.141	0.107	0.151	0.208	0.158	0.165	0.218	0.149
Throw-in attacks	-0.058	-0.063	0.070	-0.172	-0.068	-0.001	-0.091	0.054	-0.076
Throw-in attacks with shots	-0.018	0.002	0.023	-0.058	-0.019	0.054	-0.003	0.079	-0.025
% of efficiency for throw-in attacks	0.003	-0.019	-0.007	-0.029	-0.037	0.063	0.020	0.039	-0.026
Free-kick shots	0.168	0.078	0.166	0.070	0.179	0.213	0.057	0.102	0.170
Goals – Free-kick attack	0.056	0.037	0.115	0.118	0.080	0.126	0.060	0.082	0.023
% scored free kick shots	0.032	0.042	0.111	0.110	0.071	0.076	0.059	0.053	0.028

If we highlight corelations stronger than +/- 0,400 than we have different table:

Table 2.: Highlight of correlations stronger than +/- 0,400 between chances and different aspects of game per countries

CHANCES	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – left flank	0.413	0.400	0.350	0.364	0.465	0.386	0.414	0.421	0.485
Efficiency for attacks through the left flank, %	0.361	0.374	0.289	0.353	0.415	0.363	0.398	0.316	0.456
Attacks with shots – center	0.438	0.343	0.423	0.422	0.383	0.489	0.380	0.240	0.370
Attacks with shots – right flank	0.450	0.390	0.323	0.392	0.411	0.421	0.450	0.427	0.389
Efficiency for attacks through the right flank, %	0.380	0.354	0.321	0.375	0.377	0.414	0.399	0.338	0.386
Positional attacks with shots	0.579	0.547	0.512	0.547	0.532	0.558	0.568	0.505	0.537
% of efficiency for positional attacks	0.538	0.516	0.479	0.532	0.504	0.536	0.543	0.428	0.527
Counter-attacks with a shot	0.375	0.307	0.319	0.342	0.420	0.384	0.371	0.332	0.439
% of efficiency for counterattacks	0.354	0.304	0.307	0.333	0.367	0.309	0.314	0.293	0.401
Set pieces attacks	0.296	0.199	0.329	0.154	0.317	0.247	0.209	0.408	0.258
Attacks with shots – Set pieces attacks	0.429	0.327	0.381	0.384	0.448	0.393	0.359	0.496	0.397
Corner attacks	0.420	0.338	0.339	0.359	0.420	0.404	0.359	0.459	0.368
Corner attacks with shots	0.411	0.319	0.319	0.363	0.419	0.372	0.358	0.449	0.364

Highest number of correlations stronger than +/- 0,400 have Italy (9), while least France, Germany and Belgium (3). While there are some common correlations between all or most countries, like for “Positional attacks with shots” (no exclusion), “% of efficiency for positional attacks” (no exclusion), “Attacks with shots – left flank” (France, Germany and Austria excluded), “Attacks with shots – centre” (Spain, Italy, Belgium, Croatia and Poland excluded) and “Corner attacks” (Spain, France, Germany Belgium and Poland excluded). On the other side, there are some specifies for single of few countries. “Efficiency for attacks through the left flank, %” is specific for Italy and Poland, “Efficiency for attacks through the right flank, %” is specific just for Austria, “Counter-attacks with a shot” is specific just for Italy and “Set pieces attacks” just for Croatia.

Among all correlations strongest is correlation in England with “Positional attacks with shots” (0,579**).

When we look at correlations between “score a goal” and different aspects of game we have generally weaker correlations.

Table 3.: Heatmap of correlations between goals and different aspects of game per countries

GOALS	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.009	-0.006	-0.055	-0.037	-0.032	0.023	-0.030	0.085	-0.028
Attacks with shots – left flank	0.238	0.201	0.126	0.209	0.240	0.234	0.241	0.215	0.214
Efficiency for attacks through the left flank, %	0.274	0.220	0.151	0.241	0.274	0.266	0.270	0.197	0.248
Attacks – center	0.185	0.025	0.111	0.141	0.062	0.065	0.039	0.051	0.042
Attacks with shots – center	0.394	0.234	0.297	0.279	0.229	0.299	0.170	0.175	0.226
Efficiency for attacks through the central zone, %	0.322	0.222	0.258	0.233	0.224	0.297	0.180	0.168	0.228
Attacks – right flank	0.044	-0.093	-0.116	-0.108	-0.040	0.030	-0.064	0.050	-0.089
Attacks with shots – right flank	0.238	0.174	0.148	0.158	0.230	0.254	0.226	0.220	0.147
Efficiency for attacks through the right flank, %	0.271	0.221	0.217	0.222	0.263	0.277	0.268	0.209	0.193
Positional attacks	0.113	-0.046	-0.057	-0.029	-0.012	0.008	-0.078	0.097	-0.110
Positional attacks with shots	0.371	0.242	0.211	0.248	0.250	0.278	0.228	0.227	0.187
% of efficiency for positional attacks	0.410	0.281	0.267	0.289	0.281	0.323	0.289	0.226	0.241
Counter-attacks	0.047	-0.023	0.017	0.046	-0.008	0.141	0.079	0.040	0.132
Counter-attacks with a shot	0.277	0.244	0.250	0.283	0.292	0.335	0.278	0.288	0.328
% of efficiency for counterattacks	0.280	0.248	0.272	0.300	0.349	0.263	0.281	0.304	0.292
Set pieces attacks	0.016	-0.069	0.017	-0.094	-0.024	-0.054	-0.029	0.105	-0.038
Attacks with shots – Set pieces attacks	0.167	0.057	0.162	0.123	0.110	0.083	0.144	0.240	0.113
% of efficiency for set-piece attacks	0.217	0.146	0.163	0.268	0.188	0.172	0.218	0.286	0.174
Free-kick attacks	-0.090	-0.164	-0.047	-0.117	-0.142	-0.198	-0.086	-0.038	-0.054
Free-kick attacks with shots	0.033	-0.062	0.057	0.048	-0.017	-0.100	0.009	0.124	0.048
% of efficiency for free-kick attacks	0.093	0.074	0.082	0.144	0.059	0.017	0.075	0.184	0.078
Corner attacks	0.092	0.028	0.007	0.066	0.044	0.070	0.035	0.176	0.003
Corner attacks with shots	0.152	0.058	0.091	0.119	0.087	0.140	0.154	0.181	0.087
% of efficiency for corner attacks	0.121	0.069	0.122	0.111	0.104	0.136	0.158	0.153	0.130
Throw-in attacks	-0.085	-0.082	0.033	-0.212	-0.064	-0.058	-0.078	-0.079	-0.098
Throw-in attacks with shots	-0.042	-0.030	0.037	-0.123	-0.037	-0.050	-0.062	-0.030	-0.086
% of efficiency for throw-in attacks	0.000	-0.033	-0.013	-0.083	-0.034	-0.037	-0.039	-0.046	-0.119
Free-kick shots	0.009	-0.055	0.073	0.019	0.007	-0.091	0.006	0.041	0.061
Goals – Free-kick attack	0.038	0.058	0.171	0.211	0.173	0.077	0.080	0.151	0.161
% scored free kick shots	0.033	0.062	0.186	0.203	0.169	0.084	0.080	0.104	0.153

If we highlight corelations stronger than +/- 0,275 than we have different table:

Table 4.: Highlight of correlations stronger than +/-0,275 between goals and different aspects of game per countries

GOALS	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – center	0.394	0.234	0.297	0.279	0.229	0.299	0.170	0.175	0.226
Efficiency for attacks through the central zone, %	0.322	0.222	0.258	0.233	0.224	0.297	0.180	0.168	0.228
Efficiency for attacks through the right flank, %	0.271	0.221	0.217	0.222	0.263	0.277	0.268	0.209	0.193
Positional attacks with shots	0.371	0.242	0.211	0.248	0.250	0.278	0.228	0.227	0.187
% of efficiency for positional attacks	0.410	0.281	0.267	0.289	0.281	0.323	0.289	0.226	0.241
Counter-attacks with a shot	0.277	0.244	0.250	0.283	0.292	0.335	0.278	0.288	0.328
% of efficiency for counterattacks	0.280	0.248	0.272	0.300	0.349	0.263	0.281	0.304	0.292
% of efficiency for set-piece attacks	0.217	0.146	0.163	0.268	0.188	0.172	0.218	0.286	0.174

There is similarity between correlations with “create a chance” and “score a goal”. Again, England (7) and Austria (6) have most correlations above +/- 0,275, while least France (2) and Poland (2). Corelations which are most common are “% of efficiency for positional attacks” (Spain, France, Croatia and Poland excluded), “Counter-attacks with a shot” (Spain and France excluded) and “% of efficiency for counterattacks” (Spain and Austria excluded). On the other side there are some specifies like “Efficiency for attacks through the central zone, %”, “Efficiency for attacks through the right flank, %” and “Positional attacks with shots” for England and Austria and “% of efficiency for set-piece attacks” for Croatia.

Even weaker correlations are with “not defeat”.

Table 5.: Heatmap of correlations between not defeat and different aspects of game per countries

NOT DEFEAT	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.057	0.011	-0.003	0.024	0.014	0.017	-0.013	0.095	-0.024
Attacks with shots – left flank	0.209	0.105	0.057	0.132	0.183	0.152	0.121	0.198	0.165
Efficiency for attacks through the left flank, %	0.198	0.096	0.060	0.146	0.189	0.157	0.146	0.160	0.192
Attacks – center	0.172	0.004	0.090	0.099	0.111	0.028	0.064	0.096	0.084
Attacks with shots – center	0.283	0.126	0.167	0.155	0.185	0.127	0.091	0.070	0.175
Efficiency for attacks through the central zone, %	0.221	0.128	0.155	0.106	0.162	0.122	0.077	0.023	0.132
Attacks – right flank	0.088	0.005	-0.059	0.012	0.035	-0.014	0.025	0.207	-0.126
Attacks with shots – right flank	0.187	0.084	0.128	0.130	0.151	0.148	0.164	0.179	0.093
Efficiency for attacks through the right flank, %	0.176	0.081	0.159	0.142	0.142	0.187	0.171	0.121	0.131
Positional attacks	0.144	-0.003	-0.011	0.034	0.042	-0.048	-0.019	0.193	-0.141
Positional attacks with shots	0.265	0.098	0.120	0.167	0.170	0.140	0.099	0.181	0.114
% of efficiency for positional attacks	0.260	0.097	0.144	0.172	0.176	0.186	0.120	0.126	0.163
Counter-attacks	0.108	0.041	0.049	0.118	0.127	0.156	0.151	0.119	0.211
Counter-attacks with a shot	0.258	0.178	0.161	0.153	0.255	0.201	0.221	0.192	0.276
% of efficiency for counterattacks	0.220	0.158	0.147	0.118	0.240	0.140	0.215	0.169	0.211
Set pieces attacks	0.073	-0.071	0.043	-0.046	0.037	-0.011	-0.008	0.124	-0.009
Attacks with shots – Set pieces attacks	0.172	0.030	0.096	0.113	0.098	-0.007	0.099	0.229	0.032
% of efficiency for set-piece attacks	0.174	0.082	0.055	0.204	0.100	0.034	0.134	0.222	0.073
Free-kick attacks	-0.049	-0.097	-0.027	-0.106	-0.060	-0.130	-0.074	-0.035	-0.053
Free-kick attacks with shots	0.029	-0.040	0.044	0.005	0.017	-0.129	0.013	0.082	-0.012
% of efficiency for free-kick attacks	0.049	0.050	0.092	0.068	0.051	-0.043	0.068	0.105	0.026
Corner attacks	0.134	-0.040	0.035	0.034	0.081	0.095	0.035	0.164	0.039
Corner attacks with shots	0.183	0.039	0.045	0.118	0.071	0.092	0.089	0.200	0.073
% of efficiency for corner attacks	0.139	0.070	0.011	0.092	0.050	0.092	0.095	0.137	0.085
Throw-in attacks	-0.040	-0.016	0.050	-0.051	-0.034	-0.041	-0.013	0.029	-0.045
Throw-in attacks with shots	-0.018	-0.007	0.018	0.014	-0.001	-0.087	-0.007	0.038	-0.107
% of efficiency for throw-in attacks	0.013	-0.014	-0.040	0.027	-0.005	-0.110	0.020	-0.011	-0.135
Free-kick shots	0.023	-0.077	0.029	-0.012	0.043	-0.050	0.008	-0.017	0.034
Goals – Free-kick attack	0.052	-0.021	0.095	0.065	0.047	0.025	0.024	0.058	0.054
% scored free kick shots	0.056	-0.013	0.085	0.053	0.045	0.039	0.024	0.038	0.048

If we highlight correlations stronger than +/- 0,200 we will see that overall number of correlations is smaller than for previous two criteriums.

 Table 5.: Highlight of correlations stronger than +/- 0,200 between not defeat and different aspects of game per countries

NOT DEFEAT	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – left flank	0.209	0.105	0.057	0.132	0.183	0.152	0.121	0.198	0.165
Attacks with shots – center	0.283	0.126	0.167	0.155	0.185	0.127	0.091	0.070	0.175
Efficiency for attacks through the central zone, %	0.221	0.128	0.155	0.106	0.162	0.122	0.077	0.023	0.132
Positional attacks with shots	0.265	0.098	0.120	0.167	0.170	0.140	0.099	0.181	0.114
% of efficiency for positional attacks	0.260	0.097	0.144	0.172	0.176	0.186	0.120	0.126	0.163
Counter-attacks	0.108	0.041	0.049	0.118	0.127	0.156	0.151	0.119	0.211
Counter-attacks with a shot	0.258	0.178	0.161	0.153	0.255	0.201	0.221	0.192	0.276
% of efficiency for counterattacks	0.220	0.158	0.147	0.118	0.240	0.140	0.215	0.169	0.211
Attacks with shots – Set pieces attacks	0.172	0.030	0.096	0.113	0.098	-0.007	0.099	0.229	0.032
% of efficiency for set-piece attacks	0.174	0.082	0.055	0.204	0.100	0.034	0.134	0.222	0.073
Corner attacks with shots	0.183	0.039	0.045	0.118	0.071	0.092	0.089	0.200	0.073

England is having most correlations (7) while Spain, France and Austria have no single correlation stronger than +/- 0,200. “Counter-attacks with a shot”  (England, Italy and Poland) and “% of efficiency for counterattacks” (England, Italy, Belgium and Poland)  is most common aspect of game in correlations. Strongest correlation is and “% of efficiency for counterattacks” in Poland (0,276).

Finally, are presented correlations with “victory” which are stronger than correlations with “not defeat”.

Table 7.: Heatmap of correlations between victory and different aspects of game per countries

VICTORY	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.021	-0.014	-0.011	-0.002	-0.014	-0.020	-0.029	0.118	-0.054
Attacks with shots – left flank	0.221	0.130	0.086	0.177	0.179	0.143	0.131	0.211	0.116
Efficiency for attacks through the left flank, %	0.235	0.146	0.080	0.201	0.191	0.169	0.161	0.174	0.151
Attacks – center	0.154	0.070	0.086	0.095	0.076	0.045	0.051	0.088	0.049
Attacks with shots – center	0.281	0.173	0.158	0.164	0.196	0.206	0.071	0.174	0.214
Efficiency for attacks through the central zone, %	0.228	0.143	0.135	0.125	0.180	0.200	0.060	0.139	0.213
Attacks – right flank	0.056	-0.065	-0.130	-0.072	-0.019	0.033	0.001	0.101	-0.115
Attacks with shots – right flank	0.183	0.131	0.084	0.122	0.195	0.127	0.176	0.186	0.115
Efficiency for attacks through the right flank, %	0.179	0.161	0.162	0.167	0.206	0.133	0.184	0.166	0.169
Positional attacks	0.115	-0.018	-0.074	-0.006	0.010	-0.065	-0.021	0.155	-0.139
Positional attacks with shots	0.287	0.146	0.113	0.188	0.183	0.107	0.108	0.185	0.099
% of efficiency for positional attacks	0.296	0.171	0.166	0.215	0.197	0.148	0.133	0.153	0.155
Counter-attacks	0.042	0.000	0.075	0.036	0.022	0.228	0.068	0.074	0.122
Counter-attacks with a shot	0.228	0.225	0.161	0.174	0.277	0.326	0.208	0.319	0.325
% of efficiency for counterattacks	0.228	0.222	0.149	0.179	0.300	0.214	0.221	0.316	0.311
Set pieces attacks	-0.002	-0.101	-0.013	-0.114	-0.055	-0.037	-0.086	0.044	-0.066
Attacks with shots – Set pieces attacks	0.142	0.011	0.050	0.059	0.036	-0.001	0.065	0.194	0.004
% of efficiency for set-piece attacks	0.195	0.085	0.039	0.194	0.122	0.023	0.157	0.264	0.076
Free-kick attacks	-0.135	-0.200	-0.051	-0.112	-0.156	-0.169	-0.098	-0.075	-0.099
Free-kick attacks with shots	0.003	-0.088	0.009	0.047	-0.029	-0.115	0.006	0.068	-0.047
% of efficiency for free-kick attacks	0.085	0.049	0.027	0.135	0.056	-0.018	0.074	0.112	0.017
Corner attacks	0.113	0.006	-0.022	-0.006	0.035	0.109	-0.025	0.117	0.010
Corner attacks with shots	0.144	0.030	-0.005	0.018	0.032	0.073	0.070	0.199	0.044
% of efficiency for corner attacks	0.097	0.022	0.000	0.027	0.041	0.035	0.087	0.198	0.080
Throw-in attacks	-0.099	-0.059	0.025	-0.148	-0.083	-0.081	-0.087	-0.066	-0.099
Throw-in attacks with shots	0.005	-0.022	0.015	-0.053	-0.044	-0.067	-0.072	-0.085	-0.103
% of efficiency for throw-in attacks	0.045	-0.017	-0.023	-0.030	-0.056	-0.082	-0.033	-0.130	-0.126
Free-kick shots	0.054	-0.088	0.039	0.045	-0.019	-0.103	0.024	0.017	0.000
Goals – Free-kick attack	0.055	-0.030	0.089	0.146	0.087	0.072	0.045	0.075	0.100
% scored free kick shots	0.046	-0.027	0.113	0.131	0.090	0.079	0.052	0.038	0.094

If we highlight correlations stronger than +/- 0,200 we will see that overall number of correlations is higher than for previous criterium.

Table 7.: Highlight of correlations between victory and different aspects of game per countries

VICTORY	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – left flank	0.221	0.130	0.086	0.177	0.179	0.143	0.131	0.211	0.116
Efficiency for attacks through the left flank, %	0.235	0.146	0.080	0.201	0.191	0.169	0.161	0.174	0.151
Attacks with shots – center	0.281	0.173	0.158	0.164	0.196	0.206	0.071	0.174	0.214
Efficiency for attacks through the central zone, %	0.228	0.143	0.135	0.125	0.180	0.200	0.060	0.139	0.213
Efficiency for attacks through the right flank, %	0.179	0.161	0.162	0.167	0.206	0.133	0.184	0.166	0.169
Positional attacks with shots	0.287	0.146	0.113	0.188	0.183	0.107	0.108	0.185	0.099
% of efficiency for positional attacks	0.296	0.171	0.166	0.215	0.197	0.148	0.133	0.153	0.155
Counter-attacks	0.042	0.000	0.075	0.036	0.022	0.228	0.068	0.074	0.122
Counter-attacks with a shot	0.228	0.225	0.161	0.174	0.277	0.326	0.208	0.319	0.325
% of efficiency for counterattacks	0.228	0.222	0.149	0.179	0.300	0.214	0.221	0.316	0.311
% of efficiency for set-piece attacks	0.195	0.085	0.039	0.194	0.122	0.023	0.157	0.264	0.076

Again, England have most correlations (8), while France has no correlations stronger than +/- 0,200. Most common correlations are with “Counter-attacks with a shot” and “% of efficiency for counterattacks” (France and Germany excluded for both). Strongest correlation is with “Counter-attacks with a shot” in Austria.

Models for observed criteriums

Following models will indicate to us what is specific for reaching a criterium. For each criterium, models are presented for each country. In each of tables for regression models, green will be marked aspects of the game which have statistically positive significant correlation, red will be marked aspects of the game which have statistically negative significant correlation and those aspects of the game which don’t have any statistically significant correlation will not be marked.

It is interesting that in some country’s models have fewer independent variables and in some more. It might be that in countries with less variables, each of those independent variables are more influential on dependent variable than in models with more independent variables in the model. It means that experts would have less variables to focus if they would use this model to improve chances, goals, not being defeated or winning. Also seeing variables in chances, one can see where clubs from certain country are generally focused to create chances from. It might be interesting for planning matches against such clubs. Of course, it is not the same for all clubs from certain league since it can oscillate significantly but still it might open some new perspectives on playing style from clubs of observed leagues.

Regression models for “create a chance”

Adjusted R square is strong (very high) for “create chance” criterium, for all countries above 0,800. It goes from 0,810 in France till 0.851 in Belgium. Although some variables are unique present in all models, there are differences between leagues. All leagues uniquely have key passes accurate and shots on target in the model, almost all have entrance to the penalty box (Germany don’t) and shots on post/bar (England don’t).

England is country with 10 variables in the model. It has one variable which have negative significant correlation with chances in England, red cards and it should be reduced to achieve more chances logically. Also, it is uniqueness of their model together with blocked shots and defensive challenges won.

England 2021/22

Adj. R sq. 0,824

Key passes accurate

Shots

Blocked shots

Shots wide

Attacks with shots – Set pieces attacks

Free-kick shots

Entrance to the penalty box

Shots on target

Red cards

Defensive challenges won

In England highest correlation with “create a chance” have “Positional attacks with shots” (0.579) and “% of efficiency for positional attacks” (0,538). Highlighted correlation in England are “Attacks with shots – left flank”, “Attacks with shots – center”, “Attacks with shots – right flank”, “Attacks with shots – Set pieces attacks”, “Corner attacks” and “Corner attacks with shots”. Only “Attacks with shots – Set pieces attacks” are present both in model and significant correlation. In this model there is “red card” aspect coloured in red which means this aspect should be reduced to reach a criterium in opposite to green ones which should be intensified.

Spain is country with 15 variables in the model. Uniqueness of their model is attacking challenges won despite big number of variables.

Spain 2021/22

Adj. R sq. 0,817

Key passes accurate

Shots

Shots on target

Entrance to the penalty box

Shots on post / bar

Attacks with shots – Set pieces attacks

Shots wide

Attacking challenges won

Free ball pick ups

% scored free kick shots

Free-kick shots

Free-kick attacks with shots

Corner attacks with shots

Throw-in attacks with shots

Ball recoveries in opponent’s half

In Spain highest correlation with “create a chance” have “Positional attacks with shots” (0.547) and “% of efficiency for positional attacks” (0,516). Highlighted correlation in Spain are “Attacks with shots – left flank”, “Attacks with shots – center”, “Attacks with shots – right flank”, “Attacks with shots – Set pieces attacks”, “Corner attacks” and “Corner attacks with shots”.

France is country with 13 variables in the model and the smallest adjusted R square number. Uniqueness of their model are % of efficiency for corner attacks and % of efficiency for throw-in attacks.

France 2021/22

Adj. R sq. 0,810

Key passes accurate

Shots on target

Entrance to the penalty box

Attacks with shots – Set pieces attacks

Shots wide

Shots on post / bar

Goals – Free-kick attack

Crosses

% scored free kick shots

% of efficiency for throw-in attacks

Tactics

Ball recoveries in opponent’s half

% of efficiency for corner attacks

In France highest correlation with “create a chance” have “Positional attacks with shots” (0.512) and “% of efficiency for positional attacks” (0,479). Highlighted correlation in France is just one more – “Attacks with shots – center”.

Germany is country with 12 variables in the model. Uniqueness of their model are dribbles successful and fouls. Fouls are having negative significant Pearson correlation with chances.

Germany 2021/22

Adj. R sq. 0,834

Key passes accurate

Shots

Shots on target

Shots on post / bar

Attacks with shots – Set pieces attacks

Dribbles successful

Key passes

Accurate crosses, %

Free ball pick ups

Fouls

Goals – Free-kick attack

Free-kick attacks

In Germany highest correlation with “create a chance” have “Positional attacks with shots” (0.547) and “% of efficiency for positional attacks” (0,532). Highlighted correlation in Germany is, as in France just one more – “Attacks with shots – center”.

Italy is country with most variables (18) in the model. Uniqueness of their model are crosses accurate, corner attacks, counter-attacks with a shot and ball recoveries.

Italy 2021/22

Adj. R sq. 0,841

Key passes accurate

Shots on target

Entrance to the penalty box

Shots on post / bar

Attacks with shots – Set pieces attacks

Free ball pick ups

Corner attacks

Counter-attacks with a shot

Key passes

Crosses

Crosses accurate

Free-kick shots

Free-kick attacks with shots

Corner attacks with shots

Throw-in attacks with shots

Shots wide

Ball recoveries in opponent’s half

In Italy highest correlation with “create a chance” have “Positional attacks with shots” (0.532) and “% of efficiency for positional attacks” (0,504). Highlighted correlation in Spain are “Attacks with shots – left flank”, “Efficiency for attacks through the left flank, %”, “Attacks with shots – right flank”, “Counter-attacks with a shot“, “Attacks with shots – Set pieces attacks”, “Corner attacks” and “Corner attacks with shots”. Only “Attacks with shots – Set pieces attacks” and “Counter-attacks with a shot” are present both in model and significant correlation.

Austria has less variables than average (9) and have uniqueness in Ball possession, sec., Throw-in attacks and Ball possession, %.

Austria 2021/22

Adj. R sq. 0,822

Key passes accurate

Shots

Shots on target

Free-kick shots

Entrance to the penalty box

Shots on post / bar

Ball possession, sec

Throw-in attacks

Ball possession, %

In Austria highest correlation with “create a chance” have “Positional attacks with shots” (0.558) and “% of efficiency for positional attacks” (0,536). Highlighted correlation in Austria are “Attacks with shots – center”, “Attacks with shots – right flank”, “Efficiency for attacks through the right flank, %” and “Corner attacks”.

Belgium is country with least variables (8) in the model and highest adjusted R square. There is no uniqueness in their model. They have tactics as well as France.

Belgium 2021/22

Adj. R sq. 0,851

Key passes accurate

Shots on target

Entrance to the penalty box

Shots on post / bar

Attacks with shots – Set pieces attacks

Tactics

Crosses

Shots wide

In Belgium highest correlation with “create a chance” have “Positional attacks with shots” (0.568) and “% of efficiency for positional attacks” (0,543). Highlighted correlation in Belgium are “Attacks with shots – right flank”, “Set pieces attacks”, “Attacks with shots – Set pieces attacks”, “Corner attacks” and “Corner attacks with shots”. Only “Attacks with shots – Set pieces attacks” are present both in model and significant correlation.

Croatia is country with 10 variables in the model. Uniqueness of their model is tackles, air challenges won, % and positional attack.

Croatia 2021/22

Adj. R sq. 0,829

Key passes accurate

Shots on target

Shots on post / bar

Entrance to the penalty box

Goals – Free-kick attack

Attacks with shots – Set pieces attacks

Tackles

Accurate crosses, %

Air challenges won, %

Positional attacks

In Croatia highest correlation with “create a chance” have “Positional attacks with shots” (0.505) and Attacks with shots – Set pieces attacks” (0,496). Highlighted correlation in Croatia are “Attacks with shots – right flank”, “Set pieces attacks”, “% of efficiency for positional attacks”, “Corner attacks” and “Corner attacks with shots”. Only “Attacks with shots – Set pieces attacks” are present both in model and significant correlation.

Poland is country with 12 variables in the model. Uniqueness of their model is key passes, attacks with shots – left flank, % of efficiency for set-piece attacks and challenges in attack won, %.

Poland 2021/22

Adj. R sq. 0,820

Key passes accurate

Shots on target

Shots

Shots on post / bar

Entrance to the penalty box

Free-kick shots

Crosses

Key passes

Counter-attacks

Attacks with shots – left flank

% of efficiency for set-piece attacks

Challenges in attack won, %

In Poland highest correlation with “create a chance” have “Positional attacks with shots” (0.537) and Attacks with shots – Set pieces attacks” (0,527). Highlighted correlation in Poland are “Attacks with shots – left flank”, “Efficiency for attacks through the left flank, %”, “Counter-attacks with a shot” and “% of efficiency for counterattacks”.

Regression models for “score a goal”

To score a goal is more difficult to predict than to make chances. Still, adjusted R square is still moderate to strong around 0,500. On the other side, in models for “goals” is much more variables with negative significant Pearson correlation and every model have at least one of such variables, so, have in mind to see those variables as those which should be reduced in potential game plan. Variable which is present in all models is shots on target.

England has 11 variables in the model. Uniqueness of their model are ball recoveries in opponent’s half and crosses accurate.

England 2021/22

Adj. R sq. 0,497

Shots on target

Crosses

Key passes accurate

Lost balls

Defensive challenges won

Corners

Ball recoveries in opponent’s half

Opponent’s passes per defensive action

Crosses accurate

Offsides

Free-kick shots

In England highest correlation with “score a goal” have “% of efficiency for positional attacks” (0.410). Highlighted correlation in England are “Attacks with shots – center”, “Efficiency for attacks through the central zone, %”, “Efficiency for attacks through the right flank, %”, “Positional attacks with shots”, “Counter-attacks with a shot” and “% of efficiency for counterattacks”. In this model there is “lost balls” and “offsides” red marked. So, if you want to score a goal, reduce lost balls and offsides if you are playing in England. Let’s see is are the same aspects of the game indicative in the other countries.

Spain has 17 variables in the model. Uniqueness of their model are entrance to the penalty box and accurate passes.

Spain 2021/22

Adj. R sq. 0,498

Shots on target

Crosses

Key passes accurate

Shots

Opponent’s passes per defensive action

Defensive challenges won

Free-kick attacks

Ball possession, sec

Ball possession, %

Entrance to the penalty box

Corners

Positional attacks

Ball recoveries

Lost balls

Accurate passes, %

Accurate passes

% scored free kick shots

In Spain there is no significant correlation stronger than +/- 0,275. Still be aware of red ones in the model 😉 and enhance green ones.

France has 13 variables in the model. Uniqueness of their model are team pressing, challenges won, % and efficiency for attacks through the central zone, %.

France 2021/22

Adj. R sq. 0,463

Shots on target

Crosses

Key passes accurate

% scored free kick shots

Yellow cards

Lost balls

Corners

Opponent’s passes per defensive action

Team pressing

Challenges won, %

Shots on target, %

Efficiency for attacks through the central zone, %

Defensive challenges won

In France highest correlation with “score a goal” have “Attacks with shots – center” (0.297). Highlighted correlation in France is “% of efficiency for counterattacks”. “Lost balls”, “Crosses” and “yellow cards” are to be reduced.

Germany has 15 variables in the model and highest adjusted R square number. Uniqueness of their model are attacking challenges and average duration of ball possession, min.

Germany 2021/22

Adj. R sq. 0,525

Shots on target

Crosses

Key passes

Attacking challenges

Goals – Free-kick attack

Shots on target, %

% of efficiency for set-piece attacks

% of efficiency for counterattacks

Ball possession, %

Ball possession, sec

Average duration of ball possession, min

Ball possessions, quantity

Ball recoveries

Offsides

Yellow cards

In Germany highest correlation with “score a goal” have “% of efficiency for counterattacks” (0.300). Highlighted correlation in Germany are “Attacks with shots – center”, “% of efficiency for positional attacks” and “Counter-attacks with a shot”. “Ball possessions, quantity”, “Crosses” and “yellow cards” are to be reduced.

Italy has 20 variables in the model and highest adjusted R square number. Uniqueness of their model are set pieces attacks, challenges in attack won, %, free ball pick ups, throw-in attacks, tackles won, %, efficiency for attacks through the left flank, % and efficiency for attacks through the right flank, %.

Italy 2021/22

Adj. R sq. 0,483

Shots on target

Set pieces attacks

Goals – Free-kick attack

Key passes

Crosses

% of efficiency for counterattacks

Shots wide

Entrance to the penalty box

Dribbles

Challenges in attack won, %

Opponent’s passes per defensive action

Free ball pick ups

Corner attacks

Free-kick attacks

Throw-in attacks

Tackles won, %

Shots on target, %

Efficiency for attacks through the left flank, %

% of efficiency for set-piece attacks

Efficiency for attacksthrough the right flank, %

In Italy highest correlation with “score a goal” have “% of efficiency for counterattacks” (0.349). Highlighted correlation in Germany are “% of efficiency for positional attacks” and “Counter-attacks with a shot”. Interesting, red marked in Italy are “Free ball pick ups” and “Free-kick attacks”.

Austria has 15 variables in the model and smallest adjusted R square. Uniqueness of their model are ball recoveries in opponent’s half, total actions, and dribbles.

Austria 2021/22

Adj. R sq. 0,441

Shots on target

Positional attacks

Free-kick shots

Key passes accurate

Throw-in attacks with shots

Ball recoveries in opponent’s half

Goals – Free-kick attack

Opponent’s passes per defensive action

Ball possessions, quantity

Total actions

Ball recoveries

Dribbles

Ball possession, %

Free-kick attacks

Ball possession, sec

In Austria highest correlation with “score a goal” have “Counter-attacks with a shot” (0.335). Highlighted correlation in Austria are “Attacks with shots – center”, “Efficiency for attacks through the central zone, %”, “Efficiency for attacks through the right flank, %”, “Positional attacks with shots” and “% of efficiency for positional attacks”. “Free-kick attacks” are to be reduced.

Belgium has most variables in the model (21). Uniqueness of their model are ball recoveries in opponent’s half, total actions and dribbles.

Belgium 2021/22

Adj. R sq. 0,473

Shots on target

Crosses

Lost balls

Key passes

Corner attacks

Yellow cards

Team pressing successful

Accurate passes, %

Free-kick attacks

Ball possession, %

Ball possession, sec

Ball recoveries

Opponent’s passes per defensive action

Free ball pick ups

Shots wide

% of efficiency for corner attacks

% of efficiency for counterattacks

Blocked shots

% of efficiency for positional attacks

Attacks with shots – Set pieces attacks

Throw-in attacks with shots

In Belgium highest correlation with “score a goal” have “% of efficiency for positional attacks” (0.289). Highlighted correlation in Belgium are “Counter-attacks with a shot” and “% of efficiency for counterattacks”. “Crosses”, “Lost balls”, “Yellow cards” and “Free-kick attacks” are to be reduced.

Croatia has the least number of variables (9) in the model. Uniqueness of their model is air challenges.

Croatia 2021/22

Adj. R sq. 0,449

Shots on target

Key passes accurate

Crosses

Goals – Free-kick attack

Air challenges

% of efficiency for set-piece attacks

Shots wide

% of efficiency for counterattacks

Fouls

In Croatia highest correlation with “score a goal” have “% of efficiency for counterattacks” (0.304). Highlighted correlation in Croatia are “Counter-attacks with a shot” and “% of efficiency for set-piece attacks”. “Crosses” is to be reduced.

Poland has 12 variables in the model. Uniqueness of their model are % of efficiency for throw-in attacks and counter-attacks with a shot.

Poland 2021/22

Adj. R sq. 0,465

Shots on target

Crosses

Key passes accurate

Shots wide

Lost balls

Ball recoveries

Goals – Free-kick attack

Corners

% of efficiency for throw-in attacks

% of efficiency for corner attacks

Counter-attacks with a shot

Fouls

In Poland highest correlation with “score a goal” have “Counter-attacks with a shot” (0.328). Highlighted correlation in Poland is “% of efficiency for counterattacks”. “Crosses”, “Lost balls” and “% of efficiency for throw-in attacks” is to be reduced.

Regression models for “not defeat”

Not to be defeated is most difficult to predict of all four observed variables. Adjusted R square is weak, just beneath 0,300 with few exceptions. Models for “not defeat” have least variables per model. In this models, negative significant Pearson correlation is present except for Croatia. Have in mind to see those variables as those which should be reduced in potential game plan. There is no single variable present in all models.

England has 15 variables in the model and highest adjusted R square. Uniqueness of their model are entrance to the penalty box, defensive challenges won, offsides, tackles successful, yellow cards and average duration of ball possession, min.

England 2021/22

Adj. R sq. 0,299

Shots on target

Opponent’s passes per defensive action

Crosses accurate

Key passes accurate

Entrance to the penalty box

Crosses

Ball interceptions

Ball recoveries in opponent’s half

Lost balls

Defensive challenges won

“

Offsides

Tackles successful

Yellow cards

Average duration of ball possession, min

In England highest correlation with “not defeat” have “Attacks with shots – center” (0.283). Highlighted correlation in England are “Attacks with shots – left flank”, “Efficiency for attacks through the central zone, %”, “Positional attacks with shots”, “% of efficiency for positional attacks”, “Counter-attacks with a shot” and “% of efficiency for counterattacks”. In this model red marked are “Opponent’s passes per defensive action”, “Lost balls”, “Lost balls” and “Offsides”. So, if you want not to be defeated, reduce them.

Spain has 13 variables in the model. Uniqueness of their model are ball possessions – quantity, attacks – centre, defensive challenges and attacking challenges.

Spain 2021/22

Adj. R sq. 0,231

Shots on target

Opponent’s passes per defensive action

Crosses

Key passes

Ball recoveries

Ball possessions, quantity

Fouls

Attacks – center

Defensive challenges

Attacking challenges

Dribbles successful

Shots wide

Shots on target, %

In Spain there is no significant correlation above +/- 0,200.  In this model red marked are “Opponent’s passes per defensive action” and “Crosses”.

France has least number of variables (6) in the model. This model has no uniqueness.

France 2021/22

Adj. R sq. 0,165

Shots on target

Challenges won, %

Crosses

Ball interceptions

Key passes accurate

Entrance to the penalty box

In France there is also no significant correlation above +/- 0,200.  In this model there red marked is “Crosses”.

Germany has most variables (18) variables in the model and smallest adjusted R square. It has most unique variables of all models for “not defeat”. Uniqueness of their model are set pieces attacks, challenges, total actions, shots on post / bar, accurate passes, shots and entrances to the opposition half.

Germany 2021/22

Adj. R sq. 0,292

Shots on target

Opponent’s passes per defensive action

Set pieces attacks

Key passes

Accurate passes, %

Successful actions, %

% of efficiency for set-piece attacks

Challenges

Total actions

Challenges won

Shots on post / bar

Accurate passes

Ball recoveries

Shots

Entrances to the opposition half

Positional attacks

Free-kick attacks with shots

Air challenges won, %

In Germany there is just one significant correlation with “not defeat” – “% of efficiency for set-piece attacks” (0.204). In this model red marked is “Opponent’s passes per defensive action”.

Italy has 10 variables in the model. This model has no uniqueness.

Italy 2021/22

Adj. R sq. 0,228

Shots on target

Opponent’s passes per defensive action

% of efficiency for counterattacks

Challenges won, %

Crosses

Key passes

Red cards

Ball recoveries in opponent’s half

Lost balls

Ball recoveries

In Italy there are two significant correlation with “not defeat” – “Counter-attacks with a shot” (0.255) and “% of efficiency for counterattacks”. In this model red marked are “Opponent’s passes per defensive action” and “Red cards”.

Austria has 13 variables in the model. Uniqueness of their model are % of efficiency for throw-in attacks, team pressing successful and air challenges.

Austria 2021/22

Adj. R sq. 0,282

Key passes accurate

Fouls

Shots on target, %

% of efficiency for throw-in attacks

Challenges won, %

Free-kick attacks with shots

Crosses accurate

Team pressing successful

Lost balls

Ball recoveries

Accurate passes, %

Successful actions, %

Air challenges

In Austria there is no significant correlation above +/- 0,200.  In this model red marked are “% of efficiency for throw-in attacks”, “Free-kick attacks with shots” and “Lost balls”.

Belgium has 12 variables in the model. Uniqueness of their model is pressing efficiency, %.

Belgium 2021/22

Adj. R sq. 0,231

Shots on target

Ball interceptions

Key passes

Crosses

Air challenges won, %

% of efficiency for counterattacks

Ball recoveries

Dribbles successful

Pressing efficiency, %

Lost balls in own half

Challenges won, %

Shots on target, %

In Belgium there is just one significant correlation with “not defeat” – “% of efficiency for counterattacks” (0.215). In this model red marked is “Crosses”.

Croatia has 7 variables in the model. Uniqueness of their model are air challenges won and efficiency for attacks through the left flank, %.

Croatia 2021/22

Adj. R sq. 0,221

Shots on target

Air challenges won

% of efficiency for set-piece attacks

Key passes accurate

Shots wide

Ball recoveries in opponent’s half

Efficiency for attacks through the left flank, %

In Croatia highest correlation with “not defeat” have “Attacks with shots – Set pieces attacks” (0.229). Highlighted correlation in Croatia are “% of efficiency for set-piece attacks” and “Corner attacks with shots”. In this model there is no red marked aspect of the game.

Poland has 10 variables in the model. Uniqueness of their model are lost balls in own half, free-kick attacks and tactics.

Poland 2021/22

Adj. R sq. 0,248

Shots on target

Positional attacks

Ball recoveries

Lost balls in own half

% of efficiency for throw-in attacks

Challenges won

Key passes accurate

Crosses

Free-kick attacks

Tactics

In Poland highest correlation with “not defeat” have “Counter-attacks with a shot” (0.276). Highlighted correlation in Croatia are “Counter-attacks” and “% of efficiency for counterattacks”. In this model red marked aspects of the game are “Positional attacks”, “Lost balls in own half”, “% of efficiency for throw-in attacks” and “Crosses”.

Regression models for victory

Although all of models has it purpose in planning the game, and it might be that is best to observe them all in planning, victory might be the most interesting because victory brings most to the team, players, coach, and the club. Adjusted R square is between moderate and weak, from 0,225 to 0,364. There is no single variable which is present in all models. So, lets see “how to win a match in England”

England has 17 variables in the model. Uniqueness of their model are successful actions, defensive challenges won, total actions, entrances to the final third and challenges.

England 2021/22

Adj. R sq. 0,355

Shots on target

Crosses

Successful actions

Air challenges won, %

Free-kick attacks

% of efficiency for throw-in attacks

Lost balls in own half

Red cards

Defensive challenges won

Key passes accurate

Total actions

Tackles successful

Entrances to the final third

Crosses accurate

Challenges

Opponent’s passes per defensive action

Team pressing

In England highest correlation with “victory” have “% of efficiency for positional attacks” (0.296). Highlighted correlation in England are “Attacks with shots – left flank”, “Efficiency for attacks through the left flank, %”, “Attacks with shots – center”, “Efficiency for attacks through the central zone, %”, “Positional attacks with shots “, “Counter-attacks with a shot” and “% of efficiency for counterattacks”. In this model red marked are “Free-kick attacks”, “Lost balls in own half”, “Red cards”, “Crosses accurate” and “Opponent’s passes per defensive action”. So, if you want to win the match, reduce them and enhance the green ones.

Spain has the most variables (19) of all models. Uniqueness of their model are air challenges won, entrances to the opposition half, offsides and shots.

Spain 2021/22

Adj. R sq. 0,331

Shots on target

Crosses

Key passes

Opponent’s passes per defensive action

Free-kick attacks

Air challenges won

Ball interceptions

Successful actions, %

Fouls

Red cards

Dribbles successful

Attacking challenges won

Counter-attacks with a shot

Entrances to the opposition half

Offsides

Set pieces attacks

Ball recoveries

Lost balls

Shots

In Spain highest correlation with “victory” have “Counter-attacks with a shot” (0.225). Highlighted correlation in Spain is “% of efficiency for counterattacks”. In this model red marked are “Set pieces attacks” and “Lost balls”.

France has 9 of all models. Uniqueness of their model are corner attacks with shots and yellow cards.

France 2021/22

Adj. R sq. 0,241

Shots on target

Crosses

Key passes accurate

Challenges won, %

Ball interceptions

Red cards

Tactics

Corner attacks with shots

Yellow cards

In France there is no significant aspect with more strength than +/- 0,200. In this model red marked are “Crosses”, “Yellow cards” and “Red cards”.

Germany has least variables (6) of all models and weakest model. Uniqueness of their model are challenges in defence won – %, goals – Free-kick attack and shots on post / bar.

Germany 2021/22

Adj. R sq. 0,225

Shots on target

Set pieces attacks

Key passes accurate

Crosses

Challenges in defence won, %

Opponent’s passes per defensive action

Goals – Free-kick attack

Shots on post / bar

In Germany there is just two aspects of the game with correlation with “victory” above +/- 0,200 – “% of efficiency for positional attacks” (0.215) and “Efficiency for attacks through the left flank, %”. In this model red marked are “Set pieces attacks”, “Crosses” and “Opponent’s passes per defensive action”.

Italy has 12 of all models. Uniqueness of their model are accurate passes – %, free ball pick ups – %, and scored free kick shots.

Italy 2021/22

Adj. R sq. 0,301

Shots on target

Crosses

Key passes

Successful actions, %

Opponent’s passes per defensive action

Accurate passes, %

% of efficiency for counterattacks

Free-kick attacks

Air challenges

Free ball pick ups

% scored free kick shots

Tackles successful

In Italy highest correlation with “victory” have “% of efficiency for counterattacks” (0.300). Highlighted correlation in Italy are “Efficiency for attacks through the right flank, %” and “Counter-attacks with a shot”. In this model red marked are “Crosses”, “Opponent’s passes per defensive action”, “Free-kick attacks” and “Free ball pick ups”.

Austria has 12 variables in the model. Uniqueness of their model are shots on target – %, accurate crosses – %, and attacks – centre.

Austria 2021/22

Adj. R sq. 0,304

Counter-attacks with a shot

Key passes accurate

Shots on target, %

Crosses accurate

Red cards

Accurate crosses, %

Tactics

Lost balls in own half

Ball recoveries

Lost balls

Fouls

Attacks – center

In Austria highest correlation with “victory” have “Counter-attacks with a shot” (0.326). Highlighted correlation in Austria are “Attacks with shots – center”, “Efficiency for attacks through the central zone, %”, “Counter-attacks” and “% of efficiency for counterattacks”. In this model red marked is “Red cards”.

Belgium has 12 variables in the model. Uniqueness of their model are challenges in defence won – %, tackles and attacks with shots – centre.

Belgium 2021/22

Adj. R sq. 0,265

Shots on target

Crosses

Key passes

Team pressing

Set pieces attacks

Challenges in defence won,%

Dribbles successful

Tackles

% of efficiency for counterattacks

Attacks with shots – center

Attacking challenges won

Tackles successful

In Belgium highest correlation with “victory” have “% of efficiency for counterattacks” (0.221). Highlighted correlation in Belgium is “Counter-attacks with a shot”. In this model red marked are “Set pieces attacks” and “Crosses”.

Croatia has 10 variables in the model and strongest model. Uniqueness of their model are % of efficiency for set-piece attacks and ball recoveries in opponent’s half.

Croatia 2021/22

Adj. R sq. 0,364

Shots on target

Key passes accurate

% of efficiency for counterattacks

Crosses accurate

% of efficiency for set-piece attacks

% of efficiency for throw-in attacks

Ball interceptions

Ball recoveries in opponent’s half

Shots wide

Air challenges won, %

In Croatia highest correlation with “victory” have “Counter-attacks with a shot” (0.319). Highlighted correlation in Croatia are “Attacks with shots – left flank”, “% of efficiency for counterattacks” and “% of efficiency for set-piece attacks”. In this model red marked is “% of efficiency for throw-in attacks”.

Poland has 12 of all models. There is no uniqueness of their model.

Poland 2021/22

Adj. R sq. 0,306

Shots on target

Crosses

Counter-attacks with a shot

Shots wide

Team pressing

Challenges won, %

“Ball interceptions

Lost balls

Ball recoveries

% of efficiency for throw-in attacks

Air challenges

Free-kick attacks

In Poland highest correlation with “victory” have “Counter-attacks with a shot” (0.325). Highlighted correlation in Croatia are “Attacks with shots – center”, “Efficiency for attacks through the central zone, %” and “% of efficiency for counterattacks”. In this model red marked are “Crosses”, “Lost balls”, “% of efficiency for throw-in attacks” and “Free-kick attacks”.

Check out this article on LinkedIn: https://www.linkedin.com/pulse/do-leagues-generally-differ-between-themselves-pitch-slaven-marasovi%C4%87/

Have you ever wondered do leagues between themselves differ between each other based on the overall performance on the pitch? I mean of course they differ, if you have team with 100 times more values than team in another league, but how do they differ? And how would you identify the differences between leagues?

I have tested nine leagues in last season (2020/2021) by different aspects of the game: England, Spain, German, Italy, France, Belgium, Austria, Croatia and Poland. And I have used four criteriums to identify those differences:

1. What are key factors to create a chance in the individual league?

2. What are key factors on the pitch for scoring a goal in the individual league?

3. What are key factors on the pitch for not losing the game in the individual league?

4. What are key factors on the pitch for winning a match in individual league?

To identify those goals, I have used:

1. frequency (what is most used in each league),
2. statistical correlation and
3. statistical regression.

The frequency of 104 observed aspects of the game will tell us which aspect of the match is happening the most. That will not help us understanding how to reach each of criterium.

On the opposite, statistical correlation will tell us what correlate with each of criterium and help us started understanding each of criteriums per country. So, more interesting will be those aspects of the game which have stronger correlation with each of criterium.

Regression models will indicate us what is specific for reaching each of criteriums per each country and help us better understand each of criteriums. For best understanding for each country will be summarized correlations and regression models as preparation for interpretation.

For all observed dependent variables (criteriums) aim was to get the highest possible R number with the same methodology for all observed dependent variables and for all leagues. R number explains the power of the model. It lies between 0 and 1. Higher the number more precise model. It actually explains the number of variations in the dependent variable which are explained with the independent variables. Adjusted R square is R number multiplied with adjustment factor created by comparison different regression models with different independent variables.

All variables in model are statistically significant, and those which are positively statistically significant in Pearson correlation are marked green and those which are negatively statistically significant in Pearson correlation are marked red. Those which are not statistically significant in Pearson correlation are not marked. Mostly VIF number is below 5, almost all bellow 10, and rarely above 10.

Finally, I will leave each of you to give “sweet” interpretation of results. And would like to hear your thoughts about interpretation from your side 😉

In each of following heatmap and highlight map correlation of different aspects of game are presented in tables. So, let’s start with correlations between “creating a chance” and different aspects of game per countries.

Table 1.: Heatmap of correlations between chances and different aspects of game per countries

CHANCES	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.178	0.158	0.160	0.078	0.197	0.199	0.098	0.252	0.145
Attacks with shots – left flank	0.413	0.400	0.350	0.364	0.465	0.386	0.414	0.421	0.485
Efficiency for attacks through the left flank, %	0.361	0.374	0.289	0.353	0.415	0.363	0.398	0.316	0.456
Attacks – center	0.201	0.121	0.233	0.257	0.111	0.264	0.196	0.088	0.108
Attacks with shots – center	0.438	0.343	0.423	0.422	0.383	0.489	0.380	0.240	0.370
Efficiency for attacks through the central zone, %	0.373	0.300	0.348	0.333	0.360	0.397	0.342	0.222	0.332
Attacks – right flank	0.267	0.118	0.069	0.122	0.131	0.192	0.178	0.278	0.113
Attacks with shots – right flank	0.450	0.390	0.323	0.392	0.411	0.421	0.450	0.427	0.389
Efficiency for attacks through the right flank, %	0.380	0.354	0.321	0.375	0.377	0.414	0.399	0.338	0.386
Positional attacks	0.354	0.240	0.229	0.235	0.213	0.311	0.229	0.324	0.176
Positional attacks with shots	0.579	0.547	0.512	0.547	0.532	0.558	0.568	0.505	0.537
% of efficiency for positional attacks	0.538	0.516	0.479	0.532	0.504	0.536	0.543	0.428	0.527
Counter-attacks	0.122	0.032	0.085	0.099	0.169	0.188	0.179	0.159	0.159
Counter-attacks with a shot	0.375	0.307	0.319	0.342	0.420	0.384	0.371	0.332	0.439
% of efficiency for counterattacks	0.354	0.304	0.307	0.333	0.367	0.309	0.314	0.293	0.401
Set pieces attacks	0.296	0.199	0.329	0.154	0.317	0.247	0.209	0.408	0.258
Attacks with shots – Set pieces attacks	0.429	0.327	0.381	0.384	0.448	0.393	0.359	0.496	0.397
% of efficiency for set-piece attacks	0.292	0.261	0.167	0.317	0.293	0.287	0.253	0.282	0.280
Free-kick attacks	-0.022	-0.054	0.073	-0.036	0.032	-0.039	-0.008	0.129	0.010
Free-kick attacks with shots	0.170	0.111	0.170	0.170	0.203	0.147	0.137	0.231	0.182
% of efficiency for free-kick attacks	0.192	0.166	0.148	0.214	0.201	0.155	0.151	0.187	0.232
Corner attacks	0.420	0.338	0.339	0.359	0.420	0.404	0.359	0.459	0.368
Corner attacks with shots	0.411	0.319	0.319	0.363	0.419	0.372	0.358	0.449	0.364
% of efficiency for corner attacks	0.162	0.141	0.107	0.151	0.208	0.158	0.165	0.218	0.149
Throw-in attacks	-0.058	-0.063	0.070	-0.172	-0.068	-0.001	-0.091	0.054	-0.076
Throw-in attacks with shots	-0.018	0.002	0.023	-0.058	-0.019	0.054	-0.003	0.079	-0.025
% of efficiency for throw-in attacks	0.003	-0.019	-0.007	-0.029	-0.037	0.063	0.020	0.039	-0.026
Free-kick shots	0.168	0.078	0.166	0.070	0.179	0.213	0.057	0.102	0.170
Goals – Free-kick attack	0.056	0.037	0.115	0.118	0.080	0.126	0.060	0.082	0.023
% scored free kick shots	0.032	0.042	0.111	0.110	0.071	0.076	0.059	0.053	0.028

If we highlight corelations stronger than +/- 0,400 than we have different table:

Table 2.: Highlight of correlations stronger than +/- 0,400 between chances and different aspects of game per countries

CHANCES	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – left flank	0.413	0.400	0.350	0.364	0.465	0.386	0.414	0.421	0.485
Efficiency for attacks through the left flank, %	0.361	0.374	0.289	0.353	0.415	0.363	0.398	0.316	0.456
Attacks with shots – center	0.438	0.343	0.423	0.422	0.383	0.489	0.380	0.240	0.370
Attacks with shots – right flank	0.450	0.390	0.323	0.392	0.411	0.421	0.450	0.427	0.389
Efficiency for attacks through the right flank, %	0.380	0.354	0.321	0.375	0.377	0.414	0.399	0.338	0.386
Positional attacks with shots	0.579	0.547	0.512	0.547	0.532	0.558	0.568	0.505	0.537
% of efficiency for positional attacks	0.538	0.516	0.479	0.532	0.504	0.536	0.543	0.428	0.527
Counter-attacks with a shot	0.375	0.307	0.319	0.342	0.420	0.384	0.371	0.332	0.439
% of efficiency for counterattacks	0.354	0.304	0.307	0.333	0.367	0.309	0.314	0.293	0.401
Set pieces attacks	0.296	0.199	0.329	0.154	0.317	0.247	0.209	0.408	0.258
Attacks with shots – Set pieces attacks	0.429	0.327	0.381	0.384	0.448	0.393	0.359	0.496	0.397
Corner attacks	0.420	0.338	0.339	0.359	0.420	0.404	0.359	0.459	0.368
Corner attacks with shots	0.411	0.319	0.319	0.363	0.419	0.372	0.358	0.449	0.364

Highest number of correlations stronger than +/- 0,400 have Italy (9), while least France, Germany and Belgium (3). While there are some common correlations between all or most countries, like for “Positional attacks with shots” (no exclusion), “% of efficiency for positional attacks” (no exclusion), “Attacks with shots – left flank” (France, Germany and Austria excluded), “Attacks with shots – centre” (Spain, Italy, Belgium, Croatia and Poland excluded) and “Corner attacks” (Spain, France, Germany Belgium and Poland excluded). On the other side, there are some specifies for single of few countries. “Efficiency for attacks through the left flank, %” is specific for Italy and Poland, “Efficiency for attacks through the right flank, %” is specific just for Austria, “Counter-attacks with a shot” is specific just for Italy and “Set pieces attacks” just for Croatia.

Among all correlations strongest is correlation in England with “Positional attacks with shots” (0,579**).

When we look at correlations between “score a goal” and different aspects of game we have generally weaker correlations.

Table 3.: Heatmap of correlations between goals and different aspects of game per countries

GOALS	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.009	-0.006	-0.055	-0.037	-0.032	0.023	-0.030	0.085	-0.028
Attacks with shots – left flank	0.238	0.201	0.126	0.209	0.240	0.234	0.241	0.215	0.214
Efficiency for attacks through the left flank, %	0.274	0.220	0.151	0.241	0.274	0.266	0.270	0.197	0.248
Attacks – center	0.185	0.025	0.111	0.141	0.062	0.065	0.039	0.051	0.042
Attacks with shots – center	0.394	0.234	0.297	0.279	0.229	0.299	0.170	0.175	0.226
Efficiency for attacks through the central zone, %	0.322	0.222	0.258	0.233	0.224	0.297	0.180	0.168	0.228
Attacks – right flank	0.044	-0.093	-0.116	-0.108	-0.040	0.030	-0.064	0.050	-0.089
Attacks with shots – right flank	0.238	0.174	0.148	0.158	0.230	0.254	0.226	0.220	0.147
Efficiency for attacks through the right flank, %	0.271	0.221	0.217	0.222	0.263	0.277	0.268	0.209	0.193
Positional attacks	0.113	-0.046	-0.057	-0.029	-0.012	0.008	-0.078	0.097	-0.110
Positional attacks with shots	0.371	0.242	0.211	0.248	0.250	0.278	0.228	0.227	0.187
% of efficiency for positional attacks	0.410	0.281	0.267	0.289	0.281	0.323	0.289	0.226	0.241
Counter-attacks	0.047	-0.023	0.017	0.046	-0.008	0.141	0.079	0.040	0.132
Counter-attacks with a shot	0.277	0.244	0.250	0.283	0.292	0.335	0.278	0.288	0.328
% of efficiency for counterattacks	0.280	0.248	0.272	0.300	0.349	0.263	0.281	0.304	0.292
Set pieces attacks	0.016	-0.069	0.017	-0.094	-0.024	-0.054	-0.029	0.105	-0.038
Attacks with shots – Set pieces attacks	0.167	0.057	0.162	0.123	0.110	0.083	0.144	0.240	0.113
% of efficiency for set-piece attacks	0.217	0.146	0.163	0.268	0.188	0.172	0.218	0.286	0.174
Free-kick attacks	-0.090	-0.164	-0.047	-0.117	-0.142	-0.198	-0.086	-0.038	-0.054
Free-kick attacks with shots	0.033	-0.062	0.057	0.048	-0.017	-0.100	0.009	0.124	0.048
% of efficiency for free-kick attacks	0.093	0.074	0.082	0.144	0.059	0.017	0.075	0.184	0.078
Corner attacks	0.092	0.028	0.007	0.066	0.044	0.070	0.035	0.176	0.003
Corner attacks with shots	0.152	0.058	0.091	0.119	0.087	0.140	0.154	0.181	0.087
% of efficiency for corner attacks	0.121	0.069	0.122	0.111	0.104	0.136	0.158	0.153	0.130
Throw-in attacks	-0.085	-0.082	0.033	-0.212	-0.064	-0.058	-0.078	-0.079	-0.098
Throw-in attacks with shots	-0.042	-0.030	0.037	-0.123	-0.037	-0.050	-0.062	-0.030	-0.086
% of efficiency for throw-in attacks	0.000	-0.033	-0.013	-0.083	-0.034	-0.037	-0.039	-0.046	-0.119
Free-kick shots	0.009	-0.055	0.073	0.019	0.007	-0.091	0.006	0.041	0.061
Goals – Free-kick attack	0.038	0.058	0.171	0.211	0.173	0.077	0.080	0.151	0.161
% scored free kick shots	0.033	0.062	0.186	0.203	0.169	0.084	0.080	0.104	0.153

If we highlight corelations stronger than +/- 0,275 than we have different table:

Table 4.: Highlight of correlations stronger than +/-0,275 between goals and different aspects of game per countries

GOALS	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – center	0.394	0.234	0.297	0.279	0.229	0.299	0.170	0.175	0.226
Efficiency for attacks through the central zone, %	0.322	0.222	0.258	0.233	0.224	0.297	0.180	0.168	0.228
Efficiency for attacks through the right flank, %	0.271	0.221	0.217	0.222	0.263	0.277	0.268	0.209	0.193
Positional attacks with shots	0.371	0.242	0.211	0.248	0.250	0.278	0.228	0.227	0.187
% of efficiency for positional attacks	0.410	0.281	0.267	0.289	0.281	0.323	0.289	0.226	0.241
Counter-attacks with a shot	0.277	0.244	0.250	0.283	0.292	0.335	0.278	0.288	0.328
% of efficiency for counterattacks	0.280	0.248	0.272	0.300	0.349	0.263	0.281	0.304	0.292
% of efficiency for set-piece attacks	0.217	0.146	0.163	0.268	0.188	0.172	0.218	0.286	0.174

There is similarity between correlations with “create a chance” and “score a goal”. Again, England (7) and Austria (6) have most correlations above +/- 0,275, while least France (2) and Poland (2). Corelations which are most common are “% of efficiency for positional attacks” (Spain, France, Croatia and Poland excluded), “Counter-attacks with a shot” (Spain and France excluded) and “% of efficiency for counterattacks” (Spain and Austria excluded). On the other side there are some specifies like “Efficiency for attacks through the central zone, %”, “Efficiency for attacks through the right flank, %” and “Positional attacks with shots” for England and Austria and “% of efficiency for set-piece attacks” for Croatia.

Even weaker correlations are with “not defeat”.

Table 5.: Heatmap of correlations between not defeat and different aspects of game per countries

NOT DEFEAT	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.057	0.011	-0.003	0.024	0.014	0.017	-0.013	0.095	-0.024
Attacks with shots – left flank	0.209	0.105	0.057	0.132	0.183	0.152	0.121	0.198	0.165
Efficiency for attacks through the left flank, %	0.198	0.096	0.060	0.146	0.189	0.157	0.146	0.160	0.192
Attacks – center	0.172	0.004	0.090	0.099	0.111	0.028	0.064	0.096	0.084
Attacks with shots – center	0.283	0.126	0.167	0.155	0.185	0.127	0.091	0.070	0.175
Efficiency for attacks through the central zone, %	0.221	0.128	0.155	0.106	0.162	0.122	0.077	0.023	0.132
Attacks – right flank	0.088	0.005	-0.059	0.012	0.035	-0.014	0.025	0.207	-0.126
Attacks with shots – right flank	0.187	0.084	0.128	0.130	0.151	0.148	0.164	0.179	0.093
Efficiency for attacks through the right flank, %	0.176	0.081	0.159	0.142	0.142	0.187	0.171	0.121	0.131
Positional attacks	0.144	-0.003	-0.011	0.034	0.042	-0.048	-0.019	0.193	-0.141
Positional attacks with shots	0.265	0.098	0.120	0.167	0.170	0.140	0.099	0.181	0.114
% of efficiency for positional attacks	0.260	0.097	0.144	0.172	0.176	0.186	0.120	0.126	0.163
Counter-attacks	0.108	0.041	0.049	0.118	0.127	0.156	0.151	0.119	0.211
Counter-attacks with a shot	0.258	0.178	0.161	0.153	0.255	0.201	0.221	0.192	0.276
% of efficiency for counterattacks	0.220	0.158	0.147	0.118	0.240	0.140	0.215	0.169	0.211
Set pieces attacks	0.073	-0.071	0.043	-0.046	0.037	-0.011	-0.008	0.124	-0.009
Attacks with shots – Set pieces attacks	0.172	0.030	0.096	0.113	0.098	-0.007	0.099	0.229	0.032
% of efficiency for set-piece attacks	0.174	0.082	0.055	0.204	0.100	0.034	0.134	0.222	0.073
Free-kick attacks	-0.049	-0.097	-0.027	-0.106	-0.060	-0.130	-0.074	-0.035	-0.053
Free-kick attacks with shots	0.029	-0.040	0.044	0.005	0.017	-0.129	0.013	0.082	-0.012
% of efficiency for free-kick attacks	0.049	0.050	0.092	0.068	0.051	-0.043	0.068	0.105	0.026
Corner attacks	0.134	-0.040	0.035	0.034	0.081	0.095	0.035	0.164	0.039
Corner attacks with shots	0.183	0.039	0.045	0.118	0.071	0.092	0.089	0.200	0.073
% of efficiency for corner attacks	0.139	0.070	0.011	0.092	0.050	0.092	0.095	0.137	0.085
Throw-in attacks	-0.040	-0.016	0.050	-0.051	-0.034	-0.041	-0.013	0.029	-0.045
Throw-in attacks with shots	-0.018	-0.007	0.018	0.014	-0.001	-0.087	-0.007	0.038	-0.107
% of efficiency for throw-in attacks	0.013	-0.014	-0.040	0.027	-0.005	-0.110	0.020	-0.011	-0.135
Free-kick shots	0.023	-0.077	0.029	-0.012	0.043	-0.050	0.008	-0.017	0.034
Goals – Free-kick attack	0.052	-0.021	0.095	0.065	0.047	0.025	0.024	0.058	0.054
% scored free kick shots	0.056	-0.013	0.085	0.053	0.045	0.039	0.024	0.038	0.048

If we highlight correlations stronger than +/- 0,200 we will see that overall number of correlations is smaller than for previous two criteriums.

 Table 5.: Highlight of correlations stronger than +/- 0,200 between not defeat and different aspects of game per countries

NOT DEFEAT	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – left flank	0.209	0.105	0.057	0.132	0.183	0.152	0.121	0.198	0.165
Attacks with shots – center	0.283	0.126	0.167	0.155	0.185	0.127	0.091	0.070	0.175
Efficiency for attacks through the central zone, %	0.221	0.128	0.155	0.106	0.162	0.122	0.077	0.023	0.132
Positional attacks with shots	0.265	0.098	0.120	0.167	0.170	0.140	0.099	0.181	0.114
% of efficiency for positional attacks	0.260	0.097	0.144	0.172	0.176	0.186	0.120	0.126	0.163
Counter-attacks	0.108	0.041	0.049	0.118	0.127	0.156	0.151	0.119	0.211
Counter-attacks with a shot	0.258	0.178	0.161	0.153	0.255	0.201	0.221	0.192	0.276
% of efficiency for counterattacks	0.220	0.158	0.147	0.118	0.240	0.140	0.215	0.169	0.211
Attacks with shots – Set pieces attacks	0.172	0.030	0.096	0.113	0.098	-0.007	0.099	0.229	0.032
% of efficiency for set-piece attacks	0.174	0.082	0.055	0.204	0.100	0.034	0.134	0.222	0.073
Corner attacks with shots	0.183	0.039	0.045	0.118	0.071	0.092	0.089	0.200	0.073

England is having most correlations (7) while Spain, France and Austria have no single correlation stronger than +/- 0,200. “Counter-attacks with a shot”  (England, Italy and Poland) and “% of efficiency for counterattacks” (England, Italy, Belgium and Poland)  is most common aspect of game in correlations. Strongest correlation is and “% of efficiency for counterattacks” in Poland (0,276).

Finally, are presented correlations with “victory” which are stronger than correlations with “not defeat”.

Table 7.: Heatmap of correlations between victory and different aspects of game per countries

VICTORY	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.021	-0.014	-0.011	-0.002	-0.014	-0.020	-0.029	0.118	-0.054
Attacks with shots – left flank	0.221	0.130	0.086	0.177	0.179	0.143	0.131	0.211	0.116
Efficiency for attacks through the left flank, %	0.235	0.146	0.080	0.201	0.191	0.169	0.161	0.174	0.151
Attacks – center	0.154	0.070	0.086	0.095	0.076	0.045	0.051	0.088	0.049
Attacks with shots – center	0.281	0.173	0.158	0.164	0.196	0.206	0.071	0.174	0.214
Efficiency for attacks through the central zone, %	0.228	0.143	0.135	0.125	0.180	0.200	0.060	0.139	0.213
Attacks – right flank	0.056	-0.065	-0.130	-0.072	-0.019	0.033	0.001	0.101	-0.115
Attacks with shots – right flank	0.183	0.131	0.084	0.122	0.195	0.127	0.176	0.186	0.115
Efficiency for attacks through the right flank, %	0.179	0.161	0.162	0.167	0.206	0.133	0.184	0.166	0.169
Positional attacks	0.115	-0.018	-0.074	-0.006	0.010	-0.065	-0.021	0.155	-0.139
Positional attacks with shots	0.287	0.146	0.113	0.188	0.183	0.107	0.108	0.185	0.099
% of efficiency for positional attacks	0.296	0.171	0.166	0.215	0.197	0.148	0.133	0.153	0.155
Counter-attacks	0.042	0.000	0.075	0.036	0.022	0.228	0.068	0.074	0.122
Counter-attacks with a shot	0.228	0.225	0.161	0.174	0.277	0.326	0.208	0.319	0.325
% of efficiency for counterattacks	0.228	0.222	0.149	0.179	0.300	0.214	0.221	0.316	0.311
Set pieces attacks	-0.002	-0.101	-0.013	-0.114	-0.055	-0.037	-0.086	0.044	-0.066
Attacks with shots – Set pieces attacks	0.142	0.011	0.050	0.059	0.036	-0.001	0.065	0.194	0.004
% of efficiency for set-piece attacks	0.195	0.085	0.039	0.194	0.122	0.023	0.157	0.264	0.076
Free-kick attacks	-0.135	-0.200	-0.051	-0.112	-0.156	-0.169	-0.098	-0.075	-0.099
Free-kick attacks with shots	0.003	-0.088	0.009	0.047	-0.029	-0.115	0.006	0.068	-0.047
% of efficiency for free-kick attacks	0.085	0.049	0.027	0.135	0.056	-0.018	0.074	0.112	0.017
Corner attacks	0.113	0.006	-0.022	-0.006	0.035	0.109	-0.025	0.117	0.010
Corner attacks with shots	0.144	0.030	-0.005	0.018	0.032	0.073	0.070	0.199	0.044
% of efficiency for corner attacks	0.097	0.022	0.000	0.027	0.041	0.035	0.087	0.198	0.080
Throw-in attacks	-0.099	-0.059	0.025	-0.148	-0.083	-0.081	-0.087	-0.066	-0.099
Throw-in attacks with shots	0.005	-0.022	0.015	-0.053	-0.044	-0.067	-0.072	-0.085	-0.103
% of efficiency for throw-in attacks	0.045	-0.017	-0.023	-0.030	-0.056	-0.082	-0.033	-0.130	-0.126
Free-kick shots	0.054	-0.088	0.039	0.045	-0.019	-0.103	0.024	0.017	0.000
Goals – Free-kick attack	0.055	-0.030	0.089	0.146	0.087	0.072	0.045	0.075	0.100
% scored free kick shots	0.046	-0.027	0.113	0.131	0.090	0.079	0.052	0.038	0.094

If we highlight correlations stronger than +/- 0,200 we will see that overall number of correlations is higher than for previous criterium.

Table 7.: Highlight of correlations between victory and different aspects of game per countries

VICTORY	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – left flank	0.221	0.130	0.086	0.177	0.179	0.143	0.131	0.211	0.116
Efficiency for attacks through the left flank, %	0.235	0.146	0.080	0.201	0.191	0.169	0.161	0.174	0.151
Attacks with shots – center	0.281	0.173	0.158	0.164	0.196	0.206	0.071	0.174	0.214
Efficiency for attacks through the central zone, %	0.228	0.143	0.135	0.125	0.180	0.200	0.060	0.139	0.213
Efficiency for attacks through the right flank, %	0.179	0.161	0.162	0.167	0.206	0.133	0.184	0.166	0.169
Positional attacks with shots	0.287	0.146	0.113	0.188	0.183	0.107	0.108	0.185	0.099
% of efficiency for positional attacks	0.296	0.171	0.166	0.215	0.197	0.148	0.133	0.153	0.155
Counter-attacks	0.042	0.000	0.075	0.036	0.022	0.228	0.068	0.074	0.122
Counter-attacks with a shot	0.228	0.225	0.161	0.174	0.277	0.326	0.208	0.319	0.325
% of efficiency for counterattacks	0.228	0.222	0.149	0.179	0.300	0.214	0.221	0.316	0.311
% of efficiency for set-piece attacks	0.195	0.085	0.039	0.194	0.122	0.023	0.157	0.264	0.076

Again, England have most correlations (8), while France has no correlations stronger than +/- 0,200. Most common correlations are with “Counter-attacks with a shot” and “% of efficiency for counterattacks” (France and Germany excluded for both). Strongest correlation is with “Counter-attacks with a shot” in Austria.

Models for observed criteriums

Following models will indicate to us what is specific for reaching a criterium. For each criterium, models are presented for each country. In each of tables for regression models, green will be marked aspects of the game which have statistically positive significant correlation, red will be marked aspects of the game which have statistically negative significant correlation and those aspects of the game which don’t have any statistically significant correlation will not be marked.

It is interesting that in some country’s models have fewer independent variables and in some more. It might be that in countries with less variables, each of those independent variables are more influential on dependent variable than in models with more independent variables in the model. It means that experts would have less variables to focus if they would use this model to improve chances, goals, not being defeated or winning. Also seeing variables in chances, one can see where clubs from certain country are generally focused to create chances from. It might be interesting for planning matches against such clubs. Of course, it is not the same for all clubs from certain league since it can oscillate significantly but still it might open some new perspectives on playing style from clubs of observed leagues.

Regression models for “create a chance”

Adjusted R square is strong (very high) for “create chance” criterium, for all countries above 0,800. It goes from 0,810 in France till 0.851 in Belgium. Although some variables are unique present in all models, there are differences between leagues. All leagues uniquely have key passes accurate and shots on target in the model, almost all have entrance to the penalty box (Germany don’t) and shots on post/bar (England don’t).

England is country with 10 variables in the model. It has one variable which have negative significant correlation with chances in England, red cards and it should be reduced to achieve more chances logically. Also, it is uniqueness of their model together with blocked shots and defensive challenges won.

England 2021/22

Adj. R sq. 0,824

Key passes accurate

Shots

Blocked shots

Shots wide

Attacks with shots – Set pieces attacks

Free-kick shots

Entrance to the penalty box

Shots on target

Red cards

Defensive challenges won

In England highest correlation with “create a chance” have “Positional attacks with shots” (0.579) and “% of efficiency for positional attacks” (0,538). Highlighted correlation in England are “Attacks with shots – left flank”, “Attacks with shots – center”, “Attacks with shots – right flank”, “Attacks with shots – Set pieces attacks”, “Corner attacks” and “Corner attacks with shots”. Only “Attacks with shots – Set pieces attacks” are present both in model and significant correlation. In this model there is “red card” aspect coloured in red which means this aspect should be reduced to reach a criterium in opposite to green ones which should be intensified.

Spain is country with 15 variables in the model. Uniqueness of their model is attacking challenges won despite big number of variables.

Spain 2021/22

Adj. R sq. 0,817

Key passes accurate

Shots

Shots on target

Entrance to the penalty box

Shots on post / bar

Attacks with shots – Set pieces attacks

Shots wide

Attacking challenges won

Free ball pick ups

% scored free kick shots

Free-kick shots

Free-kick attacks with shots

Corner attacks with shots

Throw-in attacks with shots

Ball recoveries in opponent’s half

In Spain highest correlation with “create a chance” have “Positional attacks with shots” (0.547) and “% of efficiency for positional attacks” (0,516). Highlighted correlation in Spain are “Attacks with shots – left flank”, “Attacks with shots – center”, “Attacks with shots – right flank”, “Attacks with shots – Set pieces attacks”, “Corner attacks” and “Corner attacks with shots”.

France is country with 13 variables in the model and the smallest adjusted R square number. Uniqueness of their model are % of efficiency for corner attacks and % of efficiency for throw-in attacks.

France 2021/22

Adj. R sq. 0,810

Key passes accurate

Shots on target

Entrance to the penalty box

Attacks with shots – Set pieces attacks

Shots wide

Shots on post / bar

Goals – Free-kick attack

Crosses

% scored free kick shots

% of efficiency for throw-in attacks

Tactics

Ball recoveries in opponent’s half

% of efficiency for corner attacks

In France highest correlation with “create a chance” have “Positional attacks with shots” (0.512) and “% of efficiency for positional attacks” (0,479). Highlighted correlation in France is just one more – “Attacks with shots – center”.

Germany is country with 12 variables in the model. Uniqueness of their model are dribbles successful and fouls. Fouls are having negative significant Pearson correlation with chances.

Germany 2021/22

Adj. R sq. 0,834

Key passes accurate

Shots

Shots on target

Shots on post / bar

Attacks with shots – Set pieces attacks

Dribbles successful

Key passes

Accurate crosses, %

Free ball pick ups

Fouls

Goals – Free-kick attack

Free-kick attacks

In Germany highest correlation with “create a chance” have “Positional attacks with shots” (0.547) and “% of efficiency for positional attacks” (0,532). Highlighted correlation in Germany is, as in France just one more – “Attacks with shots – center”.

Italy is country with most variables (18) in the model. Uniqueness of their model are crosses accurate, corner attacks, counter-attacks with a shot and ball recoveries.

Italy 2021/22

Adj. R sq. 0,841

Key passes accurate

Shots on target

Entrance to the penalty box

Shots on post / bar

Attacks with shots – Set pieces attacks

Free ball pick ups

Corner attacks

Counter-attacks with a shot

Key passes

Crosses

Crosses accurate

Free-kick shots

Free-kick attacks with shots

Corner attacks with shots

Throw-in attacks with shots

Shots wide

Ball recoveries in opponent’s half

In Italy highest correlation with “create a chance” have “Positional attacks with shots” (0.532) and “% of efficiency for positional attacks” (0,504). Highlighted correlation in Spain are “Attacks with shots – left flank”, “Efficiency for attacks through the left flank, %”, “Attacks with shots – right flank”, “Counter-attacks with a shot“, “Attacks with shots – Set pieces attacks”, “Corner attacks” and “Corner attacks with shots”. Only “Attacks with shots – Set pieces attacks” and “Counter-attacks with a shot” are present both in model and significant correlation.

Austria has less variables than average (9) and have uniqueness in Ball possession, sec., Throw-in attacks and Ball possession, %.

Austria 2021/22

Adj. R sq. 0,822

Key passes accurate

Shots

Shots on target

Free-kick shots

Entrance to the penalty box

Shots on post / bar

Ball possession, sec

Throw-in attacks

Ball possession, %

In Austria highest correlation with “create a chance” have “Positional attacks with shots” (0.558) and “% of efficiency for positional attacks” (0,536). Highlighted correlation in Austria are “Attacks with shots – center”, “Attacks with shots – right flank”, “Efficiency for attacks through the right flank, %” and “Corner attacks”.

Belgium is country with least variables (8) in the model and highest adjusted R square. There is no uniqueness in their model. They have tactics as well as France.

Belgium 2021/22

Adj. R sq. 0,851

Key passes accurate

Shots on target

Entrance to the penalty box

Shots on post / bar

Attacks with shots – Set pieces attacks

Tactics

Crosses

Shots wide

In Belgium highest correlation with “create a chance” have “Positional attacks with shots” (0.568) and “% of efficiency for positional attacks” (0,543). Highlighted correlation in Belgium are “Attacks with shots – right flank”, “Set pieces attacks”, “Attacks with shots – Set pieces attacks”, “Corner attacks” and “Corner attacks with shots”. Only “Attacks with shots – Set pieces attacks” are present both in model and significant correlation.

Croatia is country with 10 variables in the model. Uniqueness of their model is tackles, air challenges won, % and positional attack.

Croatia 2021/22

Adj. R sq. 0,829

Key passes accurate

Shots on target

Shots on post / bar

Entrance to the penalty box

Goals – Free-kick attack

Attacks with shots – Set pieces attacks

Tackles

Accurate crosses, %

Air challenges won, %

Positional attacks

In Croatia highest correlation with “create a chance” have “Positional attacks with shots” (0.505) and Attacks with shots – Set pieces attacks” (0,496). Highlighted correlation in Croatia are “Attacks with shots – right flank”, “Set pieces attacks”, “% of efficiency for positional attacks”, “Corner attacks” and “Corner attacks with shots”. Only “Attacks with shots – Set pieces attacks” are present both in model and significant correlation.

Poland is country with 12 variables in the model. Uniqueness of their model is key passes, attacks with shots – left flank, % of efficiency for set-piece attacks and challenges in attack won, %.

Poland 2021/22

Adj. R sq. 0,820

Key passes accurate

Shots on target

Shots

Shots on post / bar

Entrance to the penalty box

Free-kick shots

Crosses

Key passes

Counter-attacks

Attacks with shots – left flank

% of efficiency for set-piece attacks

Challenges in attack won, %

In Poland highest correlation with “create a chance” have “Positional attacks with shots” (0.537) and Attacks with shots – Set pieces attacks” (0,527). Highlighted correlation in Poland are “Attacks with shots – left flank”, “Efficiency for attacks through the left flank, %”, “Counter-attacks with a shot” and “% of efficiency for counterattacks”.

Regression models for “score a goal”

To score a goal is more difficult to predict than to make chances. Still, adjusted R square is still moderate to strong around 0,500. On the other side, in models for “goals” is much more variables with negative significant Pearson correlation and every model have at least one of such variables, so, have in mind to see those variables as those which should be reduced in potential game plan. Variable which is present in all models is shots on target.

England has 11 variables in the model. Uniqueness of their model are ball recoveries in opponent’s half and crosses accurate.

England 2021/22

Adj. R sq. 0,497

Shots on target

Crosses

Key passes accurate

Lost balls

Defensive challenges won

Corners

Ball recoveries in opponent’s half

Opponent’s passes per defensive action

Crosses accurate

Offsides

Free-kick shots

In England highest correlation with “score a goal” have “% of efficiency for positional attacks” (0.410). Highlighted correlation in England are “Attacks with shots – center”, “Efficiency for attacks through the central zone, %”, “Efficiency for attacks through the right flank, %”, “Positional attacks with shots”, “Counter-attacks with a shot” and “% of efficiency for counterattacks”. In this model there is “lost balls” and “offsides” red marked. So, if you want to score a goal, reduce lost balls and offsides if you are playing in England. Let’s see is are the same aspects of the game indicative in the other countries.

Spain has 17 variables in the model. Uniqueness of their model are entrance to the penalty box and accurate passes.

Spain 2021/22

Adj. R sq. 0,498

Shots on target

Crosses

Key passes accurate

Shots

Opponent’s passes per defensive action

Defensive challenges won

Free-kick attacks

Ball possession, sec

Ball possession, %

Entrance to the penalty box

Corners

Positional attacks

Ball recoveries

Lost balls

Accurate passes, %

Accurate passes

% scored free kick shots

In Spain there is no significant correlation stronger than +/- 0,275. Still be aware of red ones in the model 😉 and enhance green ones.

France has 13 variables in the model. Uniqueness of their model are team pressing, challenges won, % and efficiency for attacks through the central zone, %.

France 2021/22

Adj. R sq. 0,463

Shots on target

Crosses

Key passes accurate

% scored free kick shots

Yellow cards

Lost balls

Corners

Opponent’s passes per defensive action

Team pressing

Challenges won, %

Shots on target, %

Efficiency for attacks through the central zone, %

Defensive challenges won

In France highest correlation with “score a goal” have “Attacks with shots – center” (0.297). Highlighted correlation in France is “% of efficiency for counterattacks”. “Lost balls”, “Crosses” and “yellow cards” are to be reduced.

Germany has 15 variables in the model and highest adjusted R square number. Uniqueness of their model are attacking challenges and average duration of ball possession, min.

Germany 2021/22

Adj. R sq. 0,525

Shots on target

Crosses

Key passes

Attacking challenges

Goals – Free-kick attack

Shots on target, %

% of efficiency for set-piece attacks

% of efficiency for counterattacks

Ball possession, %

Ball possession, sec

Average duration of ball possession, min

Ball possessions, quantity

Ball recoveries

Offsides

Yellow cards

In Germany highest correlation with “score a goal” have “% of efficiency for counterattacks” (0.300). Highlighted correlation in Germany are “Attacks with shots – center”, “% of efficiency for positional attacks” and “Counter-attacks with a shot”. “Ball possessions, quantity”, “Crosses” and “yellow cards” are to be reduced.

Italy has 20 variables in the model and highest adjusted R square number. Uniqueness of their model are set pieces attacks, challenges in attack won, %, free ball pick ups, throw-in attacks, tackles won, %, efficiency for attacks through the left flank, % and efficiency for attacks through the right flank, %.

Italy 2021/22

Adj. R sq. 0,483

Shots on target

Set pieces attacks

Goals – Free-kick attack

Key passes

Crosses

% of efficiency for counterattacks

Shots wide

Entrance to the penalty box

Dribbles

Challenges in attack won, %

Opponent’s passes per defensive action

Free ball pick ups

Corner attacks

Free-kick attacks

Throw-in attacks

Tackles won, %

Shots on target, %

Efficiency for attacks through the left flank, %

% of efficiency for set-piece attacks

Efficiency for attacksthrough the right flank, %

In Italy highest correlation with “score a goal” have “% of efficiency for counterattacks” (0.349). Highlighted correlation in Germany are “% of efficiency for positional attacks” and “Counter-attacks with a shot”. Interesting, red marked in Italy are “Free ball pick ups” and “Free-kick attacks”.

Austria has 15 variables in the model and smallest adjusted R square. Uniqueness of their model are ball recoveries in opponent’s half, total actions, and dribbles.

Austria 2021/22

Adj. R sq. 0,441

Shots on target

Positional attacks

Free-kick shots

Key passes accurate

Throw-in attacks with shots

Ball recoveries in opponent’s half

Goals – Free-kick attack

Opponent’s passes per defensive action

Ball possessions, quantity

Total actions

Ball recoveries

Dribbles

Ball possession, %

Free-kick attacks

Ball possession, sec

In Austria highest correlation with “score a goal” have “Counter-attacks with a shot” (0.335). Highlighted correlation in Austria are “Attacks with shots – center”, “Efficiency for attacks through the central zone, %”, “Efficiency for attacks through the right flank, %”, “Positional attacks with shots” and “% of efficiency for positional attacks”. “Free-kick attacks” are to be reduced.

Belgium has most variables in the model (21). Uniqueness of their model are ball recoveries in opponent’s half, total actions and dribbles.

Belgium 2021/22

Adj. R sq. 0,473

Shots on target

Crosses

Lost balls

Key passes

Corner attacks

Yellow cards

Team pressing successful

Accurate passes, %

Free-kick attacks

Ball possession, %

Ball possession, sec

Ball recoveries

Opponent’s passes per defensive action

Free ball pick ups

Shots wide

% of efficiency for corner attacks

% of efficiency for counterattacks

Blocked shots

% of efficiency for positional attacks

Attacks with shots – Set pieces attacks

Throw-in attacks with shots

In Belgium highest correlation with “score a goal” have “% of efficiency for positional attacks” (0.289). Highlighted correlation in Belgium are “Counter-attacks with a shot” and “% of efficiency for counterattacks”. “Crosses”, “Lost balls”, “Yellow cards” and “Free-kick attacks” are to be reduced.

Croatia has the least number of variables (9) in the model. Uniqueness of their model is air challenges.

Croatia 2021/22

Adj. R sq. 0,449

Shots on target

Key passes accurate

Crosses

Goals – Free-kick attack

Air challenges

% of efficiency for set-piece attacks

Shots wide

% of efficiency for counterattacks

Fouls

In Croatia highest correlation with “score a goal” have “% of efficiency for counterattacks” (0.304). Highlighted correlation in Croatia are “Counter-attacks with a shot” and “% of efficiency for set-piece attacks”. “Crosses” is to be reduced.

Poland has 12 variables in the model. Uniqueness of their model are % of efficiency for throw-in attacks and counter-attacks with a shot.

Poland 2021/22

Adj. R sq. 0,465

Shots on target

Crosses

Key passes accurate

Shots wide

Lost balls

Ball recoveries

Goals – Free-kick attack

Corners

% of efficiency for throw-in attacks

% of efficiency for corner attacks

Counter-attacks with a shot

Fouls

In Poland highest correlation with “score a goal” have “Counter-attacks with a shot” (0.328). Highlighted correlation in Poland is “% of efficiency for counterattacks”. “Crosses”, “Lost balls” and “% of efficiency for throw-in attacks” is to be reduced.

Regression models for “not defeat”

Not to be defeated is most difficult to predict of all four observed variables. Adjusted R square is weak, just beneath 0,300 with few exceptions. Models for “not defeat” have least variables per model. In this models, negative significant Pearson correlation is present except for Croatia. Have in mind to see those variables as those which should be reduced in potential game plan. There is no single variable present in all models.

England has 15 variables in the model and highest adjusted R square. Uniqueness of their model are entrance to the penalty box, defensive challenges won, offsides, tackles successful, yellow cards and average duration of ball possession, min.

England 2021/22

Adj. R sq. 0,299

Shots on target

Opponent’s passes per defensive action

Crosses accurate

Key passes accurate

Entrance to the penalty box

Crosses

Ball interceptions

Ball recoveries in opponent’s half

Lost balls

Defensive challenges won

“

Offsides

Tackles successful

Yellow cards

Average duration of ball possession, min

In England highest correlation with “not defeat” have “Attacks with shots – center” (0.283). Highlighted correlation in England are “Attacks with shots – left flank”, “Efficiency for attacks through the central zone, %”, “Positional attacks with shots”, “% of efficiency for positional attacks”, “Counter-attacks with a shot” and “% of efficiency for counterattacks”. In this model red marked are “Opponent’s passes per defensive action”, “Lost balls”, “Lost balls” and “Offsides”. So, if you want not to be defeated, reduce them.

Spain has 13 variables in the model. Uniqueness of their model are ball possessions – quantity, attacks – centre, defensive challenges and attacking challenges.

Spain 2021/22

Adj. R sq. 0,231

Shots on target

Opponent’s passes per defensive action

Crosses

Key passes

Ball recoveries

Ball possessions, quantity

Fouls

Attacks – center

Defensive challenges

Attacking challenges

Dribbles successful

Shots wide

Shots on target, %

In Spain there is no significant correlation above +/- 0,200.  In this model red marked are “Opponent’s passes per defensive action” and “Crosses”.

France has least number of variables (6) in the model. This model has no uniqueness.

France 2021/22

Adj. R sq. 0,165

Shots on target

Challenges won, %

Crosses

Ball interceptions

Key passes accurate

Entrance to the penalty box

In France there is also no significant correlation above +/- 0,200.  In this model there red marked is “Crosses”.

Germany has most variables (18) variables in the model and smallest adjusted R square. It has most unique variables of all models for “not defeat”. Uniqueness of their model are set pieces attacks, challenges, total actions, shots on post / bar, accurate passes, shots and entrances to the opposition half.

Germany 2021/22

Adj. R sq. 0,292

Shots on target

Opponent’s passes per defensive action

Set pieces attacks

Key passes

Accurate passes, %

Successful actions, %

% of efficiency for set-piece attacks

Challenges

Total actions

Challenges won

Shots on post / bar

Accurate passes

Ball recoveries

Shots

Entrances to the opposition half

Positional attacks

Free-kick attacks with shots

Air challenges won, %

In Germany there is just one significant correlation with “not defeat” – “% of efficiency for set-piece attacks” (0.204). In this model red marked is “Opponent’s passes per defensive action”.

Italy has 10 variables in the model. This model has no uniqueness.

Italy 2021/22

Adj. R sq. 0,228

Shots on target

Opponent’s passes per defensive action

% of efficiency for counterattacks

Challenges won, %

Crosses

Key passes

Red cards

Ball recoveries in opponent’s half

Lost balls

Ball recoveries

In Italy there are two significant correlation with “not defeat” – “Counter-attacks with a shot” (0.255) and “% of efficiency for counterattacks”. In this model red marked are “Opponent’s passes per defensive action” and “Red cards”.

Austria has 13 variables in the model. Uniqueness of their model are % of efficiency for throw-in attacks, team pressing successful and air challenges.

Austria 2021/22

Adj. R sq. 0,282

Key passes accurate

Fouls

Shots on target, %

% of efficiency for throw-in attacks

Challenges won, %

Free-kick attacks with shots

Crosses accurate

Team pressing successful

Lost balls

Ball recoveries

Accurate passes, %

Successful actions, %

Air challenges

In Austria there is no significant correlation above +/- 0,200.  In this model red marked are “% of efficiency for throw-in attacks”, “Free-kick attacks with shots” and “Lost balls”.

Belgium has 12 variables in the model. Uniqueness of their model is pressing efficiency, %.

Belgium 2021/22

Adj. R sq. 0,231

Shots on target

Ball interceptions

Key passes

Crosses

Air challenges won, %

% of efficiency for counterattacks

Ball recoveries

Dribbles successful

Pressing efficiency, %

Lost balls in own half

Challenges won, %

Shots on target, %

In Belgium there is just one significant correlation with “not defeat” – “% of efficiency for counterattacks” (0.215). In this model red marked is “Crosses”.

Croatia has 7 variables in the model. Uniqueness of their model are air challenges won and efficiency for attacks through the left flank, %.

Croatia 2021/22

Adj. R sq. 0,221

Shots on target

Air challenges won

% of efficiency for set-piece attacks

Key passes accurate

Shots wide

Ball recoveries in opponent’s half

Efficiency for attacks through the left flank, %

In Croatia highest correlation with “not defeat” have “Attacks with shots – Set pieces attacks” (0.229). Highlighted correlation in Croatia are “% of efficiency for set-piece attacks” and “Corner attacks with shots”. In this model there is no red marked aspect of the game.

Poland has 10 variables in the model. Uniqueness of their model are lost balls in own half, free-kick attacks and tactics.

Poland 2021/22

Adj. R sq. 0,248

Shots on target

Positional attacks

Ball recoveries

Lost balls in own half

% of efficiency for throw-in attacks

Challenges won

Key passes accurate

Crosses

Free-kick attacks

Tactics

In Poland highest correlation with “not defeat” have “Counter-attacks with a shot” (0.276). Highlighted correlation in Croatia are “Counter-attacks” and “% of efficiency for counterattacks”. In this model red marked aspects of the game are “Positional attacks”, “Lost balls in own half”, “% of efficiency for throw-in attacks” and “Crosses”.

Regression models for victory

Although all of models has it purpose in planning the game, and it might be that is best to observe them all in planning, victory might be the most interesting because victory brings most to the team, players, coach, and the club. Adjusted R square is between moderate and weak, from 0,225 to 0,364. There is no single variable which is present in all models. So, lets see “how to win a match in England”

England has 17 variables in the model. Uniqueness of their model are successful actions, defensive challenges won, total actions, entrances to the final third and challenges.

England 2021/22

Adj. R sq. 0,355

Shots on target

Crosses

Successful actions

Air challenges won, %

Free-kick attacks

% of efficiency for throw-in attacks

Lost balls in own half

Red cards

Defensive challenges won

Key passes accurate

Total actions

Tackles successful

Entrances to the final third

Crosses accurate

Challenges

Opponent’s passes per defensive action

Team pressing

In England highest correlation with “victory” have “% of efficiency for positional attacks” (0.296). Highlighted correlation in England are “Attacks with shots – left flank”, “Efficiency for attacks through the left flank, %”, “Attacks with shots – center”, “Efficiency for attacks through the central zone, %”, “Positional attacks with shots “, “Counter-attacks with a shot” and “% of efficiency for counterattacks”. In this model red marked are “Free-kick attacks”, “Lost balls in own half”, “Red cards”, “Crosses accurate” and “Opponent’s passes per defensive action”. So, if you want to win the match, reduce them and enhance the green ones.

Spain has the most variables (19) of all models. Uniqueness of their model are air challenges won, entrances to the opposition half, offsides and shots.

Spain 2021/22

Adj. R sq. 0,331

Shots on target

Crosses

Key passes

Opponent’s passes per defensive action

Free-kick attacks

Air challenges won

Ball interceptions

Successful actions, %

Fouls

Red cards

Dribbles successful

Attacking challenges won

Counter-attacks with a shot

Entrances to the opposition half

Offsides

Set pieces attacks

Ball recoveries

Lost balls

Shots

In Spain highest correlation with “victory” have “Counter-attacks with a shot” (0.225). Highlighted correlation in Spain is “% of efficiency for counterattacks”. In this model red marked are “Set pieces attacks” and “Lost balls”.

France has 9 of all models. Uniqueness of their model are corner attacks with shots and yellow cards.

France 2021/22

Adj. R sq. 0,241

Shots on target

Crosses

Key passes accurate

Challenges won, %

Ball interceptions

Red cards

Tactics

Corner attacks with shots

Yellow cards

In France there is no significant aspect with more strength than +/- 0,200. In this model red marked are “Crosses”, “Yellow cards” and “Red cards”.

Germany has least variables (6) of all models and weakest model. Uniqueness of their model are challenges in defence won – %, goals – Free-kick attack and shots on post / bar.

Germany 2021/22

Adj. R sq. 0,225

Shots on target

Set pieces attacks

Key passes accurate

Crosses

Challenges in defence won, %

Opponent’s passes per defensive action

Goals – Free-kick attack

Shots on post / bar

In Germany there is just two aspects of the game with correlation with “victory” above +/- 0,200 – “% of efficiency for positional attacks” (0.215) and “Efficiency for attacks through the left flank, %”. In this model red marked are “Set pieces attacks”, “Crosses” and “Opponent’s passes per defensive action”.

Italy has 12 of all models. Uniqueness of their model are accurate passes – %, free ball pick ups – %, and scored free kick shots.

Italy 2021/22

Adj. R sq. 0,301

Shots on target

Crosses

Key passes

Successful actions, %

Opponent’s passes per defensive action

Accurate passes, %

% of efficiency for counterattacks

Free-kick attacks

Air challenges

Free ball pick ups

% scored free kick shots

Tackles successful

In Italy highest correlation with “victory” have “% of efficiency for counterattacks” (0.300). Highlighted correlation in Italy are “Efficiency for attacks through the right flank, %” and “Counter-attacks with a shot”. In this model red marked are “Crosses”, “Opponent’s passes per defensive action”, “Free-kick attacks” and “Free ball pick ups”.

Austria has 12 variables in the model. Uniqueness of their model are shots on target – %, accurate crosses – %, and attacks – centre.

Austria 2021/22

Adj. R sq. 0,304

Counter-attacks with a shot

Key passes accurate

Shots on target, %

Crosses accurate

Red cards

Accurate crosses, %

Tactics

Lost balls in own half

Ball recoveries

Lost balls

Fouls

Attacks – center

In Austria highest correlation with “victory” have “Counter-attacks with a shot” (0.326). Highlighted correlation in Austria are “Attacks with shots – center”, “Efficiency for attacks through the central zone, %”, “Counter-attacks” and “% of efficiency for counterattacks”. In this model red marked is “Red cards”.

Belgium has 12 variables in the model. Uniqueness of their model are challenges in defence won – %, tackles and attacks with shots – centre.

Belgium 2021/22

Adj. R sq. 0,265

Shots on target

Crosses

Key passes

Team pressing

Set pieces attacks

Challenges in defence won,%

Dribbles successful

Tackles

% of efficiency for counterattacks

Attacks with shots – center

Attacking challenges won

Tackles successful

In Belgium highest correlation with “victory” have “% of efficiency for counterattacks” (0.221). Highlighted correlation in Belgium is “Counter-attacks with a shot”. In this model red marked are “Set pieces attacks” and “Crosses”.

Croatia has 10 variables in the model and strongest model. Uniqueness of their model are % of efficiency for set-piece attacks and ball recoveries in opponent’s half.

Croatia 2021/22

Adj. R sq. 0,364

Shots on target

Key passes accurate

% of efficiency for counterattacks

Crosses accurate

% of efficiency for set-piece attacks

% of efficiency for throw-in attacks

Ball interceptions

Ball recoveries in opponent’s half

Shots wide

Air challenges won, %

In Croatia highest correlation with “victory” have “Counter-attacks with a shot” (0.319). Highlighted correlation in Croatia are “Attacks with shots – left flank”, “% of efficiency for counterattacks” and “% of efficiency for set-piece attacks”. In this model red marked is “% of efficiency for throw-in attacks”.

Poland has 12 of all models. There is no uniqueness of their model.

Poland 2021/22

Adj. R sq. 0,306

Shots on target

Crosses

Counter-attacks with a shot

Shots wide

Team pressing

Challenges won, %

“Ball interceptions

Lost balls

Ball recoveries

% of efficiency for throw-in attacks

Air challenges

Free-kick attacks

In Poland highest correlation with “victory” have “Counter-attacks with a shot” (0.325). Highlighted correlation in Croatia are “Attacks with shots – center”, “Efficiency for attacks through the central zone, %” and “% of efficiency for counterattacks”. In this model red marked are “Crosses”, “Lost balls”, “% of efficiency for throw-in attacks” and “Free-kick attacks”.

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