Home sweet home: Quantifying home court advantages for NCAA basketball statistics

Journal of Sports Analytics ◽

10.3233/jsa-200450 ◽

2021 ◽

pp. 1-12

Author(s):

Matthew van Bommel ◽

Luke Bornn ◽

Peter Chow-White ◽

Chuancong Gao

Keyword(s):

Regression Model ◽

National Collegiate Athletic Association ◽

Score Statistic ◽

Present Evidence ◽

Home Team ◽

The Road ◽

Score Statistics ◽

Location Games ◽

On The Road ◽

Measures Of Performance

Box score statistics are the baseline measures of performance for National Collegiate Athletic Association (NCAA) basketball. Between the 2011-2012 and 2015-2016 seasons, NCAA teams performed better at home compared to on the road in nearly all box score statistics across both genders and all three divisions. Using box score data from over 100,000 games spanning the three divisions for both women and men, we examine the factors underlying this discrepancy. The prevalence of neutral location games in the NCAA provides an additional angle through which to examine the gaps in box score statistic performance, which we believe has been underutilized in existing literature. We also estimate a regression model to quantify the home court advantages for box score statistics after controlling for other factors such as number of possessions, and team strength. Additionally, we examine the biases of scorekeepers and referees. We present evidence that scorekeepers tend to have greater home team biases when observing men compared to women, higher divisions compared to lower divisions, and stronger teams compared to weaker teams. Finally, we present statistically significant results indicating referee decisions are impacted by attendance, with larger crowds resulting in greater bias in favor of the home team.

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P075 Travel or cheers? Examining the drivers and mechanisms of home court advantage in the 2020/2021 NBA regular season

SLEEP Advances ◽

10.1093/sleepadvances/zpab014.119 ◽

2021 ◽

Vol 2 (Supplement_1) ◽

pp. A45-A45

Author(s):

J Leota ◽

D Hoffman ◽

L Mascaro ◽

M Czeisler ◽

K Nash ◽

...

Keyword(s):

Random Effects ◽

Mixed Models ◽

Fixed Effects ◽

Natural Experiment ◽

National Basketball Association ◽

Home Team ◽

The Road ◽

On The Road ◽

The Impact ◽

The Relationship

Abstract Introduction Home court advantage (HCA) in the National Basketball Association (NBA) is well-documented, yet the co-occurring drivers responsible for this advantage have proven difficult to examine in isolation. The Coronavirus disease (COVID-19) pandemic resulted in the elimination of crowds in ~50% of games during the 2020/2021 NBA season, whereas travel remained unchanged. Using this ‘natural experiment’, we investigated the impact of crowds and travel-related sleep and circadian disruption on NBA HCA. Methods 1080 games from the 2020/2021 NBA regular season were analyzed using mixed models (fixed effects: crowds, travel; random effects: team, opponent). Results In games with crowds, home teams won 58.65% of the time and outrebounded (M=2.28) and outscored (M=2.18) their opponents. In games without crowds, home teams won significantly less (50.60%, p = .01) and were outrebounded (M=-0.41, p < .001) and outscored (M=-0.13, p < .05) by their opponents. Further, the increase in home rebound margin fully mediated the relationship between crowds and home points margin (p < .001). No significant sleep or circadian effects were observed. Discussion Taken together, these results suggest that HCA in the 2020/2021 NBA season was predominately driven by the presence of crowds and their influence on the effort exerted by the home team to rebound the ball. Moreover, we speculate that the strict NBA COVID-19 policies may have mitigated the travel-related sleep and circadian effects on the road team. These findings are of considerable significance to a domain wherein marginal gains can have immense competitive, financial, and even historical consequences.

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Simulation-Based Analysis of the Effect of Significant Traffic Parameters on Lane Changing for Driving Logic “Cautious” on a Freeway

Sustainability ◽

10.3390/su11215976 ◽

2019 ◽

Vol 11 (21) ◽

pp. 5976

Author(s):

Danish Farooq ◽

Janos Juhasz

Keyword(s):

Regression Model ◽

Traffic Safety ◽

Traffic Volume ◽

Lane Change ◽

Lane Changing ◽

Safety Issues ◽

The Road ◽

Speed Distribution ◽

On The Road ◽

High Traffic Volume

Lane changing of traffic flow is a complicated and significsant behavior for traffic safety on the road. Frequent lane changing can cause serious traffic safety issues, particularly on a two-lane road section of a freeway. This study aimed to analyze the effect of significant traffic parameters for traffic safety on lane change frequency using the studied calibrated values for driving logic “conscious” in VISSIM. Video-recorded traffic data were utilized to calibrate the model under specified traffic conditions, and the relationship between observed variables were estimated using simulation plots. The results revealed that changes in average desired speed and traffic volume had a positive relationship with lane change frequency. In addition, lane change frequency was observed to be higher when the speed distribution was set large. 3D surface plots were also developed to show the integrated effect of specified traffic parameters on lane change frequency. Results showed that high average desired speed and large desired speed distribution coupled with high traffic volume increased the lane change frequency tremendously. The study also attempted to develop a regression model to quantify the effect of the observed parameters on lane change frequency. The regression model results showed that desired speed distribution had the highest effect on lane change frequency compared to other traffic parameters. The findings of the current study highlight the most significant traffic parameters that influence the lane change frequency.

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Design and Development of Weigh-In-Motion Using Vehicular Telematics

Journal of Sensors ◽

10.1155/2020/7871215 ◽

2020 ◽

Vol 2020 ◽

pp. 1-22

Author(s):

Sivaramalingam Kirushanth ◽

Boniface Kabaso

Keyword(s):

Regression Model ◽

Performance Monitoring ◽

Nonlinear Regression Model ◽

Traffic Enforcement ◽

Highway Infrastructure ◽

The Road ◽

Weigh In Motion ◽

Transport Industry ◽

Nonintrusive Measurement ◽

On The Road

Identifying overloaded vehicles on a highway is essential for the safety of vehicles on the road as well as for the performance monitoring of highway infrastructure and planning. Traffic enforcement uses various weigh-in-motion (WIM) methods. Since Vehicular Telematics (VT) is favoured in the transport industry, using it for building a new WIM system to infer the payload of a vehicle at any road segment would be beneficial for the transport industry. This paper presents the effort taken to use VT data from onboard diagnostics modules and smartphones to infer the payload of a vehicle. The experiment done to find the correlation between VT data and the payload of a vehicle is discussed. Feature engineering was done; nine different settings were tested to find the best regression model. A multiple nonlinear regression model produced significant a p value of 6.322e-08 and an R-squared value of 0.8736. Results support the notion of using the VT data for nonintrusive measurement of the weight of a vehicle in motion.

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