In the nuanced world of game score modeling, the decision of how many players per team to incorporate in the model is pivotal. At FireBet, our models harness a wealth of data, including last game stats, team-level statistics, and player season averages. This article delves into the intricacies of the 'Players per Team' or 'Season Avg' metric and how it shapes our predictive models.

Section 1: The Role of Players per Team Metric

The 'Players per Team' metric specifically controls the number of season average inputs for each team. These season averages and team rosters are determined based on players' minutes played. By selecting the top players up to the chosen setting value during training, we ensure a tailored and precise data input. This selection is crucial as it directly influences the model's focus and accuracy.

Section 2: Zero Players Setting – Momentum and Recent Performance

Setting '0 players per team' offers a unique approach. This setting enables models to completely disregard season averages, thereby emphasizing momentum and recent performance. This model variant is particularly useful for capturing the dynamism of short-term form over long-term averages, offering a fresh perspective in prediction scenarios.

Section 3: Importance of the Remove Player Function

When incorporating season averages, the 'Remove Player' function becomes an indispensable tool. This feature allows for the exclusion of players, enabling multiple simulations with varying data combinations. It's especially beneficial in scenarios where key players are questionable, allowing for a more realistic and adaptable prediction model.

Section 4: Editing Season Averages for Accurate Simulations

The 'Edit Stat' feature is another critical aspect of our models. This functionality allows users to manually adjust a player’s season averages on a game-by-game basis. By editing these stats and running simulations, users can explore various outcomes and better understand the potential impact of a player who may be performing above or below their average. This feature is particularly valuable for accounting for recent changes in player performance that may not be fully captured by season-long data.

Section 5: Balancing Data for Optimal Predictions

Striking the right balance between different data points is essential. While player season averages offer a solid foundation, integrating recent performance data ensures our models remain agile and responsive to current trends. This combination allows for a more comprehensive and nuanced understanding of each team's potential performance.