# Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics
## 4 Results
### Financial Analysis
\begin{table}
\begin{tabular}{l r r r r r r r r} \hline \hline  & **RAF** & **BOO** & **SVM** & **LIR** & **ALL** & **BET** & **HOM** & **RAN** \\ \hline Mean & 0.0043 & 0.0072 & \(-\)0.0243 & \(-\)0.0068 & 0.0158 & \(-\)0.0453 & \(-\)0.0460 & \(-\)0.0817 \\ \(p\)-value of Wilcoxon-Test & 0.0000 & 0.0000 & 0.3924 & 0.0029 & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ Minimum & \(-\)1.0000 & \(-\)1.0000 & \(-\)1.0000 & \(-\)1.0000 & \(-\)1.0000 & \(-\)1.0000 & \(-\)1.0000 & \(-\)1.0000 \\ Quartile 1 & 0.0650 & 0.0600 & \(-\)1.0000 & \(-\)1.0000 & 0.0700 & \(-\)1.0000 & \(-\)1.0000 & \(-\)1.0000 \\ Median & 0.1600 & 0.1700 & 0.1800 & 0.1800 & 0.1700 & \(-\)1.0000 & \(-\)1.0000 & \(-\)1.0000 \\ Quartile 3 & 0.2500 & 0.2800 & 0.4000 & 0.3900 & 0.2500 & 0.9100 & 0.9100 & 0.8500 \\ Maximum & 1.7500 & 1.8800 & 3.0000 & 2.1000 & 1.7500 & 1.8000 & 16.0000 & 25.0000 \\ Standard deviation & 0.5131 & 0.5620 & 0.7046 & 0.6533 & 0.5132 & 0.9962 & 1.1802 & 1.4538 \\ Skewness & \(-\)1.0569 & \(-\)0.7224 & \(-\)0.1647 & \(-\)0.4736 & \(-\)1.0397 & 0.2267 & 1.3737 & 2.2716 \\ Kurtosis & 3.3994 & 3.1293 & 2.5016 & 2.2271 & 3.5229 & 1.3018 & 8.2053 & 14.4552 \\ Share with return \(>0\) & 0.8126 & 0.7912 & 0.6971 & 0.7292 & 0.8177 & 0.4991 & 0.4544 & 0.3605 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the strategies RAF, BOO, SVM, LIR, ALL, BET, HOM, and RAN. Risk and return statistics per placed bet covering football seasons 2006/2007 to 2017/2018. bets (as highlighted in Table 3). In line with our previous findings, the strategy ALL is best in class with a cumulated return value of 9.47. This is reasonable, as ALL combines multiple ML algorithms and averaging predictions leads to a reduction of error in many scenarios. In combination with the conservative betting rule (\(\pm\)2 goal difference), ALL only places a bet if either several of the single models predict the same extreme outcome, or if one model is extremely confident compared to the others. RAF, and BOO are the best single ML algorithms with an final value of 2.36 (RAF) and 4.43 (BOO). LIR and SVM both do not finish with a positive value--SVM shows a downwards trend and finishes with \(-\)30.09 whereas LIR drops only to \(-\)7.91 with a slight upwards momentum in recent seasons. Contrarily, all other strategies time series decline steadily over time. Executing BET and HOM eventually results in a cumulative return of \(-\)1859.60 and \(-\)1915.51 at the end of football season 17/18. Not considering any information at all and randomly placing bets (RAN) is worst compared to the other strategies and results in a total loss of \(-\)3301.83 over the time period considered in this article. The accuracy of each strategy is linked to the volatility of the time series--the higher the accuracy, the less volatility we find in the cumulative returns. It seems that the growth period for the sophisticated ML based strategies has taken place in earlier times before the season 2012/2013. Back then, the results do not seem to be dominated by single well executed bets with high odds, but rather are the sum of many well predicted outcomes with decent returns. Since the year 2013, the time series of the ALL, BOO and RAF strategies are flattening out. This may be the influence of better odds, as the bookmakers themselves started using machine learning algorithms which results in a more precise estimation of odds. In this case, employing a strategy using similar models would result in a zero game, which might be an explanation for the constant time series in the end. Figure 5 shows the previous results in more detail for each league. It seems that the vast majority of bets are placed in the higher leagues throughout all the strategies. This implies that either the goal differences are generally larger in first league games, or the players characteristics and skills are more influential on the final goal difference if we consider better (first league) players. Overall, most of the bets are placed in England and Spain--both two countries in which sports bets are very popular.