A regression-based predictive model of student attendance at UVA men's basketball games

2004 ◽  
Author(s):  
T.L.W. Walls ◽  
E.J. Bass
Keyword(s):  
Predictive Model ◽  
Student Attendance ◽  
Men's Basketball

scholarly journals Building an NCAA men’s basketball predictive model and quantifying its success

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael J. Lopez ◽  
Gregory J. Matthews
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Predictive Model ◽  
Loss Function ◽  
Las Vegas ◽  
Statistical Tools ◽  
Men's Basketball ◽  
Predictive Algorithms ◽  
Complex Models ◽  
Logistic Regressions

AbstractComputing and machine learning advancements have led to the creation of many cutting-edge predictive algorithms, some of which have been demonstrated to provide more accurate forecasts than traditional statistical tools. In this manuscript, we provide evidence that the combination of modest statistical methods with informative data can meet or exceed the accuracy of more complex models when it comes to predicting the NCAA men’s basketball tournament. First, we describe a prediction model that merges the point spreads set by Las Vegas sportsbooks with possession based team efficiency metrics by using logistic regressions. The set of probabilities generated from this model most accurately predicted the 2014 tournament, relative to approximately 400 competing submissions, as judged by the log loss function. Next, we attempt to quantify the degree to which luck played a role in the success of this model by simulating tournament outcomes under different sets of true underlying game probabilities. We estimate that under the most optimistic of game probability scenarios, our entry had roughly a 12% chance of outscoring all competing submissions and just less than a 50% chance of finishing with one of the ten best scores.

Declining Student Attendance at College Sporting Events: Testing the Relative Influence of Constraints

2021 ◽  
Vol 30 (2) ◽  
Author(s):  
Jason Simmons ◽  
Nels Popp ◽  
T. Christopher Greenwell
Keyword(s):  
College Students ◽  
Target Market ◽  
Sporting Events ◽  
Relative Importance ◽  
Market Segment ◽  
Student Attendance ◽  
Intercollegiate Athletic ◽  
Men's Basketball ◽  
High Profile ◽  
Important Target

College students represent an important target market for intercollegiate athletic marketers; however, re-cent years have seen a nationwide trend of declining student attendance at high-profile sporting events (Cohen, 2014; Rowland, 2019). The current study examined this issue by studying the influence of constraints on student attendance. Data were collected in partnership with the National Association of Collegiate Marketing Administrators (NACMA). In total, more than 23,000 respondents from 60 NCAA Di-vision I institutions participated in the study. Conjoint analysis was utilized to assess student attendance preferences across a set of attributes to determine the relative importance of each constraint tested. Separate analyses were conducted for both football and men’s basketball samples as well as NCAA conference tier (Power Five, Group of Five, FCS). Results indicated constraint importance varied by market segment. Of note, as passion levels among student respondents increased, importance shifted from ticket price to seat location and game day atmosphere.

908-P: Predictive Model for Glycemic Level of People with Diabetes Mellitus Using Data Extracted from Electronic Medical Records in Primary Care

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 908-P
Author(s):  
SOSTENES MISTRO ◽  
THALITA V.O. AGUIAR ◽  
VANESSA V. CERQUEIRA ◽  
KELLE O. SILVA ◽  
JOSÉ A. LOUZADO ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Primary Care ◽  
Predictive Model ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Glycemic Level ◽  
Using Data
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