## 5 Evaluation and Research Findings
This suggests a moderate decrease in goal-scoring efficiency compared to the top performers. * Player ID 2097: With an actual goals per game value of 1.13, this player is close to Player ID 2098, only 0.01 lower. However, compared to the top player (227), the difference is more pronounced, amounting to 0.28. * Player ID 2099: This player has the lowest actual goals per game value in the table at 1.10. Compared to the top player, the difference is 0.31, indicating a larger gap in goal-scoring efficiency. Overall, the numerical analysis shows a range of goals per game values among the top players, with player ID 227 leading by a considerable margin. While the other players also have high \(aGg\) values, there is a noticeable decline in efficiency as we move down the list. This analysis highlights the goal-scoring capabilities and differences in performance among the top players. Moreover, our second objective is to forecast \(xG\) to assess player performance. Table 10 presents data on the top five players based on \(xGg\). Notably, the same five players who were identified as the top performers in actual goals per game also rank highly in \(xGg\)according to our model's predictions. This alignment suggests consistency in performance across different metrics, highlighting the model's accuracy in identifying top talent. We analyze the differences in performance between the players based on their predicted expected goals per game (\(xGg\)) values. * Player ID 2371: This player leads the table with a predicted \(xGg\) per game value of 1.13. This indicates a consistent ability to generate high-quality scoring chances and outperforms other players in the table. * Player ID 2098: This player has an \(xGg\) value of 1.09, identical to Player ID 227, and is only 0.04 lower than Player ID 2371. This small difference indicates a similar level of performance in generating expected goals per game. * Player ID 227: With an \(xGg\) of 1.09, identical to Player ID 2098, this player's value is also slightly lower than Player ID 2371 by 0.04. This minor gap reflects a comparable ability to create scoring opportunities. * Player ID 2097: This player's \(xGg\)value of 0.99 shows a more noticeable drop from the top three performers, falling 0.10 behind Player ID 227. This decrease may suggest a moderate reduction in efficiency for generating quality chances. * Player ID 2099: This player has the lowest predicted \(xGg\) per game value in the table at 0.76. The difference from Player ID 2097 is 0.23, showing a significant gap in efficiency compared to the top players in terms of creating quality scoring opportunities. Overall, the numerical analysis of predicted \(xGg\) values demonstrates a range of performance levels among the top players. Player ID 2371 leads with the highest \(xGg\) value, showing a consistent ability to generate high-quality scoring opportunities. While Player IDs 2098 and 227 follow closely behind, their slight differences suggest comparable efficiency in creating \(xGg\). As we move further down the list, the efficiency in generating quality chances decreases noticeably, with Player IDs 2097 and 2099 exhibiting more significant gaps compared to the top performers. This analysis highlights the differences \begin{table}
\begin{tabular}{l l} \hline \hline
**Player ID** & **Expected Goals per Game (\(xGg\))** \\ \hline
2371 & 1.13 \\
2098 & 1.09 \\
227 & 1.09 \\
2097 & 0.99 \\
2099 & 0.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Top players with maximum predicted \(xGg\) (in descending order). in players' capabilities to create scoring opportunities, emphasizing the variations in performance across the top players. Moreover, when a player or team consistently scores more goals than their \(xGg\), it typically signifies outstanding finishing skills and effectiveness in capitalizing on scoring chances [25].