In professional basketball, the accurate prediction of scoring opportunities
based on strategic decision-making is crucial for space and player evaluations.
However, traditional models often face challenges in accounting for the
complexities of off-ball movements, which are essential for accurate predictive
performance. In this study, we propose two mathematical models to predict
off-ball scoring opportunities in basketball, considering both pass-to-score
and dribble-to-score movements: the Ball Movement for Off-ball Scoring (BMOS)
and the Ball Intercept and Movement for Off-ball Scoring (BIMOS) models. The
BMOS adapts principles from the Off-Ball Scoring Opportunities (OBSO) model,
originally designed for soccer, to basketball, whereas the BIMOS also
incorporates the likelihood of interception during ball movements. We evaluated
these models using player tracking data from 630 NBA games in the 2015-2016
regular season, demonstrating that the BIMOS outperforms the BMOS in terms of
scoring prediction accuracy. Thus, our models provide valuable insights for
tactical analysis and player evaluation in basketball.

我们提出了两个数学模型来预测篮球比赛中的无球得分机会，分别考虑了传球得分和运球得分的动作，我们利用 630 场 NBA 比赛中的球员追踪数据评估了这些模型，结果表明 BIMOS 在得分预测准确性方面优于 BMOS，因此我们的模型为篮球的战术分析和球员评估提供了有价值的见解。

篮球中无球得分预测的数学模型

Mathematical models for off-ball scoring prediction in basketball

Player tracking data remains out of reach for many professional football
teams as their video feeds are not sufficiently high quality for computer
vision technologies to be used. To help bridge this gap, we present a method
that can estimate continuous full-pitch tracking data from discrete data made
from broadcast footage. Such data could be collected by clubs or players at a
similar cost to event data, which is widely available down to semi-professional
level. We test our method using open-source tracking data, and include a
version that can be applied to a large set of over 200 games with such discrete
data.

从广播镜头制作的离散数据中，我们提出一种方法来估计连续的全场跟踪数据，帮助球队和球员收集高质量的球员跟踪数据。