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Modeling player engagement is a key challenge in games. However, the gameplay signatures of engaged players can be highly context-sensitive, varying based on where the game is used or what population of players is using it. Traditionally, models of player engagement are investigated in a particular context, and it is unclear how effectively these models generalize to other settings and populations. In this work, we investigate a Bayesian hierarchical linear model for multi-task learning to devise a model of player engagement from a pair of datasets that were gathered in two complementary contexts: a Classroom Study with middle school students and a Laboratory Study with undergraduate students. Both groups of players used similar versions of Crystal Island, an educational interactive narrative game for science learning. Results indicate that the Bayesian hierarchical model outperforms both pooled and context-specific models in cross-validation measures of predicting player motivation from in-game behaviors, particularly for the smaller Classroom Study group. Further, we find that the posterior distributions of model parameters indicate that the coefficient for a measure of gameplay performance significantly differs between groups. Drawing upon their capacity to share information across groups, hierarchical Bayesian methods provide an effective approach for modeling player engagement with data from similar, but different, contexts.
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Investigation of Advanced NBA Metrics
Currently, a variety of popular quality metrics exist for NBA players such as Value Over Replacement Player (VORP), Player Efficiency Rating (PER), and Win Shares (WS). Despite their often relatively simple formulations, less is known regarding how traditional box score statistics may disproportionately affect player quality with respect to these different measures. Using traditional per-36 minute statistics along with more modern advanced metrics from the previous 15 seasons, we predict VORP, PER, and WS using a variety of approaches ranging from linear models to more advanced methods like random forests and tree-augmented regression. In addition to assessing fit, we pay particular attention to partial effects of individual statistics on each response, allowing us to investigate which types of players are favored by different measures. Finally, we test our models and conclusions by predicting VORP, PER, and WS for a sample of top NBA players from the 2016/2017 season.
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- Award ID(s):
- 1712041
- PAR ID:
- 10160484
- Date Published:
- Journal Name:
- CMU Sports Analytics Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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