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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/prediction-models-sports-data
课程评论：没有评论
课程名称：利用体育数据进行预测模型 概述：本课程旨在教授学员如何使用Python生成专业体育赛事结果的预测。课程主要强调逻辑回归方法在建模比赛结果中的应用，利用团队支出的数据进行分析。学员将学习如何对过去的比赛结果进行建模，并使用该模型预测尚未进行的比赛结果。此外，课程还将展示如何利用赔率数据评估模型的可靠性。分析将首先应用于英超联赛，然后是NBA和NHL。课程还提供了数据分析与赌博之间关系的概述，包括其历史和与体育博彩相关的社会问题及个人风险。 课程大纲： 第一周：介绍回归模型在处理体育比赛类别结果（如胜、平、负）中的应用，解释线性概率模型（LPM）的理论基础、计算应用及其经验限制，随后引入并演示逻辑回归作为处理类别因变量的更好替代方法。 第二周：探讨概率与博彩市场之间的关系，解释赔率的概念及其与概率的关系，借助体育例子开发一个评估赔率准确性的指标，并讨论博彩市场的效率意义。 第三周：展示如何使用有序逻辑模型及可公开获取的信息来预测英超足球比赛结果，并评估这些预测结果与博彩赔率之间的准确性，结果显示预测相当准确。 第四周：通过在北美三大球队联赛（NHL、NBA、MLB）的背景下复制前一周的英超预测模型，评估其有效性。具体演示如何利用有序逻辑模型和可公开的信息来预测NHL、NBA、MLB常规赛的结果，及其与博彩赔率的准确性对比。 第五周：考察赌博的历史和社会后果，以及赌博与统计之间的关系，从不同伦理和宗教体系的角度来探讨赌博问题，并分析与问题博彩相关的议题。 该课程为学员提供了丰富的理论知识和实践技能，帮助他们在体育数据分析及博彩领域中获得更深入的理解和应用能力。
Name:Week 1
Description:This module introduces the regression models in dealing with the categorical outcome variables in sport contest (i.e., Win, Draw, Lose). It explains the Linear Probability Model (LPM) in terms of its theoretical foundations, computational applications, and empirical limitations. Then the module introduces and demonstrates the Logistic Regression as a better substitute of LPM for the categorical dependent variables.
Name:Week 2
Description:This module explores the relationship between probability and betting markets. It explains the concept of odds, and the relationship between betting odds and probabilities. It then develops a measure of the accuracy of betting odds using sports examples, and assesses the meaning of efficiency in betting markets.
Name:Week 3
Description:This module shows how to forecast the outcome of EPL soccer games using an ordered logit model and publicly available information. It assesses the accuracy of these forecasts against the betting odds and shows that they are remarkably accurate.
Name:Week 4
Description:This module assesses the efficacy of the EPL forecasting model covered in the previous week by replicating the model in the context of three North American team sports leagues (i.e., NHL, NBA, MLB). Specifically, this module shows how to forecast the outcome of NHL, NBA, MLB regular season games using an ordered logit model and publicly available information. It assesses the accuracy of these forecasts against the betting odds.
Name:Week 5
Description:In this module we examine the historical and social consequences of gambling, and the relationship between gambling and statistics. Gambling is explored from the perspective of different ethical and religious systems. Issues of problem gambling are explored and assessed.
In this course the learner will be shown how to generate forecasts of game results in professional sports using Python. The main emphasis of the course is on teaching the method of logistic regression as a way of modeling game results, using data on team expenditures. The learner is taken through the process of modeling past results, and then using the model to forecast the outcome games not yet played. The course will show the learner how to evaluate the reliability of a model using data on betting odds. The analysis is applied first to the English Premier League, then the NBA and NHL. The course also provides an overview of the relationship between data analytics and gambling, its history and the social issues that arise in relation to sports betting, including the personal risks.
