Performance and Compensation on the LPGA Tour: A Statistical Analysis

Pfitzner, C. Barry; Rishel, D. Tracy

doi:10.1080/24748668.2005.11868335

Cited by 7 publications

(4 citation statements)

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“…Nevertheless, there is a lack of fundamental data on how scores and statistics (fairway keep rate, par on rate, putting rate, etc.) vary when amateur golfers play just a single stroke versus when they are subjected to course management and strict rules but have the ability to perform VOLUME --| ISSUE -| 2020 | 3 multiple hits (Broadie and Ko, 2009;Chae et al, 2018;Dorsel and Rotunda, 2001;Fried et al, 2004;Pfitzner and Rishel, 2005).…”

Section: Introductionmentioning

confidence: 99%

Investigating factors that improve golf scores by comparing statistics of amateur golfers in repeat scramble strokes and one-ball conditions

Suzuki¹,

Sheahan²,

Okuda³

et al. 2020

jhse

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This study investigated how scores and statistics of amateur golfers changed when self-scramble (SC) was compared to normal play. Participants took two (SC2) to three (SC3) golf strokes each. Scores under these conditions were compared with those from normal round (NR) play to determine to what extent the scores and statistics would improve. Means, standard deviations, repeated measures one-way ANOVA, and a forward stepwise multiple regression analysis were performed for data analysis. The mean total score over nine holes by NR plays was 46.6 strokes, with a significant reduction of 7.7 strokes and 10.6 strokes under SC2 and SC3 conditions, respectively. Even when the three play conditions' total scores decreased significantly for all subjects, the putt rate was around 38%, suggesting that the ratio of the number of putts per score did not change when play conditions changed. Stepwise multiple regression analysis for NR and SC2 using total score as the dependent variable and each statistic as the independent variables showed that greens in regulation (GIR) predicted 51.2% of the total score for NR; GIR and the One-Putt rate predicted 87.3% of the total score for SC2. These findings indicate that GIR is the most important factor for reducing non-professional golfers' total scores. This study suggests that players gain experience in reducing their golf scores in a format that is similar to an actual game of golf.

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Section: Introductionmentioning

confidence: 99%

Investigating factors that improve golf scores by comparing statistics of amateur golfers in repeat scramble strokes and one-ball conditions

Suzuki¹,

Sheahan²,

Okuda³

et al. 2020

jhse

View full text Add to dashboard Cite

show abstract

“…Research investigating the relationship between performance in PGA, LPGA (women's), and SPGA (senior) events and various golf statistics has revealed that in PGA, driving distance contributes the most to performance. In LPGA, green in regulation, representing the percentage of times a player hits the green (e.g., three or fewer strokes for Par 5, two or fewer strokes for Par 4, and one stroke for Par 3), was identified as the most influential factor, with driving distance ranking second (Pfitzner & Rishel, 2005). However, in senior PGA, the scramble rate (the probability of making a par or birdie on a hole when not hitting the green) was reported to have the highest contribution, with driving distance ranking fourth (Fried et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Case study based on ball trajectory and motion analysis of international long-drive distance golf player

Ichikawa,

Sakai,

Suzuki

et al. 2024

J. Hum. Sport Exerc.

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This study aimed to measure the ball trajectory, club face control, and body movements of a long-drive distance golf player (LDP) who excels in international competitions. The participant randomly executed 10 shots of three trajectory types—straight, draw, and fade—using a driver in an indoor facility. The results indicated that the fade shot exhibited a significantly longer ball carry distance (S: 329.7 ± 31.7, D: 301.8 ± 30.6, F: 345.7 ± 18.4 yards). The offline distance also showed a significant increase (S: 3.6 ± 37.3, D: −12.0 ± 26.4, F: 30.8 ± 42.2 yards) for the fade shot. If shots exceeding the 60-yard width of the offline distance resulting in invalid attempts were counted, there were 2 of 10 attempts for straight, 0 of 10 attempts for draw, and 4 of 10 attempts for fade. Analysis of the ball trajectory of draw shots revealed a statistically significant trend towards lower peak height and descent angle. In all three trajectories, there were no significant differences observed in ball speed (S: 189.3±6.3, D: 192.1±5.0, F: 193.3±4.1 mph) and club head speed (S: 137.2 ± 1.6, D: 137.0 ± 1.2, F: 137.7 ± 2.2 mph). For the draw shots, the club path exhibited in-to-out trajectory, moving the club face, and the face angle was open during the ball impact. It is evident that even in golf players aiming for the maximum distance in a single shot, club head speed and grip speed tend to remain relatively constant. The findings indicate that the LDP executes precise movement and face control tailored to the three ball trajectories, demonstrating sophisticated motor control.

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“…They report that the PGA commands a more well rounded game with PUTTGIR and GIR as the two largest factors of success, while the Senior Tour and the LPGA, due to its shorter courses, emphasize iron and putting skills [10]. Pfitzner and Rishel (2005) and Shumanske (2000) also investigate determinants of golfer success in the LPGA and how they compare to the PGA [11]. As expected, the relative importance of golfer skill factors varies across tournaments.…”

Section: Variability Caused By Course Difficulty and Field Strengthmentioning

confidence: 99%

Predicting golf scores at the shot level

Drappi

Keh

2019

JSA

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Abstract. We present the only model to date that predicts the discrete probability distribution of a golfer's score for each hole of a tournament on a shot-by-shot basis. We first generalized Broadie's technique of score-based skill estimation to allow a golfer's skill (e.g. scoring average, driving spray, iron play, putting) to vary continuously by time-weighting data with exponential decay. Training a single-layer 50-node neural network to predict probabilities of scoring by hole resulted in an out-of-sample cross-entropy error of 0.974. We then added features of each hole (e.g. par, green size, sand area) onto the model, representing golfers and holes in an N-by-M dimensional space and achieved an error of 0.953. Adding in course features provided by ShotLink (e.g. fairway height, firmness, wind speed) dropped error to 0.9374. Finally, generalizing the model to update probabilities per shot further reduced error to 0.891. This work helps players understand which skill sets they should improve on, manage courses better (better to miss fairway right or left on hole 13 of Bethpage Black?) and select the best tournament to enter. It also revolutionizes the viewing experience of the PGA by live updating odds to win per shot (similar to WSOP) and helps sports books offer more accurate betting lines.

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Performance and Compensation on the LPGA Tour: A Statistical Analysis

Cited by 7 publications

References 0 publications

Investigating factors that improve golf scores by comparing statistics of amateur golfers in repeat scramble strokes and one-ball conditions

Investigating factors that improve golf scores by comparing statistics of amateur golfers in repeat scramble strokes and one-ball conditions

Case study based on ball trajectory and motion analysis of international long-drive distance golf player

Predicting golf scores at the shot level

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