# Predicting Football Match Results Using a Poisson Regression Model
## 4 Analysis of the Results
### Experimental Results
In the context of the present analysis and the test below, we made the assumption that, for five games, the parameters can be regarded as relevant and do not need updating. The results of the 50 matches of the five selected fixtures (20 to 24) as mentioned above are presented in Table 11, where the results have been rounded as in the previous tests. The model does a rather good job in estimating the goals for each team in those 50 matches as the differences were mostly in the range of 0 to 2 goals. Some deviations in the 3-goals difference refer to unanticipated results such as the the 1-1 draw of Manchester City at Nottingham Forest or the 1-0 loss of Manchester City to Tottenham. For both home and way goals, over 70% of the differences range within \(-1\) to 1, indicating that, even on an independent sample, the model performs well in predicting the goals scored by a team. Considering the above results, the model seemed to perform well in the simulation test. More specifically, for Manchester City, the goal difference between the total goals scored and those estimated by the model was 28 goals (94 to 122 by the model estimate)--where 15 were from home matches (60 observed against 75 estimated by the model) and 13 from the away scores (34 observed against 47 by the model estimate). For Fulham, a team with a mid-table finish, the model estimate was even closer than that of Manchester City. The model estimated only 20 more total goals compared to the observed (55 versus 75 by the model estimate). From those 20, 11 originated from home scores (31 observed against 42 estimated) and 9 from away (24 observed versus 33 estimated). Regarding the results for Southampton, the model was even closer for both home and away scores. The model estimated only 9 more goals for home matches (19 observed against 28 by the model estimate), and regarding away scores, the model almost exactly estimated the scores with only a 4-goal difference (17 goals versus the 21 of the model). Overall, the model estimated only 13 more goals for Southampton compared to the actual scores (36 observed versus 49 estimated). In conclusion, as the above tests indicate, our model performs well in predicting the goals that a team is likely to score in a football match. More specifically, based on the first test, the model was deemed rather successful in estimating the scores of 76 random matches, based on the parameters calculated from all matches available (380), with differences in goals for both and away teams ranging mostly between \(-1\) and 1. Similar results of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Difference** & **Home Goals** & **Home Goals’ \%** & **Away Goals** & **Away Goals’ \%** \\ \hline \(-2\) & 2 & 4\% & 1 & 2\% \\ \hline \(-1\) & 4 & 8\% & 6 & 12\% \\ \hline
0 & 19 & 38\% & 15 & 30\% \\ \hline
1 & 13 & 26\% & 17 & 34\% \\ \hline
2 & 11 & 22\% & 9 & 18\% \\ \hline
3 & 1 & 2\% & 2 & 4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Differences (model vs. observed) in goals for fixtures 20–24 (50 games in total). satisfactory performance were also observed in predicting the goals of the whole league campaign for the selected teams, as the second test presents. The estimated goals were not so far off the actual goals scored by the teams. Lastly, the results of the final test on an independent sample were also rather good, where the number of estimated goals and the observed ones were very close. Following its general simplicity in terms of application and the good performance of the algorithm, it could be a useful tool for football fans or an initial basis in formulating a betting strategy.