Drive for Show and Putt for Dough? Not Anymore

Baugher, Carson D.; Day, Jonathan; Burford, Elvin W.

doi:10.1177/1527002514528517

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…They find that pure putting skill is the most important factor that contributes to a golfer's success (measured in earnings this time) [8]. Baugher, et al (2014) employed the same featurization and analyzed PGA tournaments from 2006-2013. They also show that putting skill is the most important determinant of success [9].…”

Section: Attempts At Decreasing Correlations Between Skillsmentioning

confidence: 99%

“…With the isolation of pure golfer skill factors, both authors Alexander, et al (2005) and Baugher (2014) have also studied the change in the importance of these skills in time [8,9]. Alexander, et al (2005) notes an increase in the marginal value of DRIVEDIST and a decrease in the value of pure putting skill in the PGA from 1992 to 2001.…”

Section: Attempts At Decreasing Correlations Between Skillsmentioning

confidence: 99%

“…Alexander, et al (2005) notes an increase in the marginal value of DRIVEDIST and a decrease in the value of pure putting skill in the PGA from 1992 to 2001. Among other trends, Baugher, et al (2014) notes the increase in importance of DRIVEDIST and DRIVEACC in the PGA from 2008 to 2013. This increase in importance, as they have mentioned in their work, coincides with the decision of the PGA Tour to increase the length of the courses in time.…”

Section: Attempts At Decreasing Correlations Between Skillsmentioning

confidence: 99%

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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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Section: Attempts At Decreasing Correlations Between Skillsmentioning

confidence: 99%

Section: Attempts At Decreasing Correlations Between Skillsmentioning

confidence: 99%

Section: Attempts At Decreasing Correlations Between Skillsmentioning

confidence: 99%

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Predicting golf scores at the shot level

Drappi

Keh

2019

JSA

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“…the resulting ball flight distance is directly dependent on initial ball speed after contact, club head speed prior to contact, and the quality of the club-ball interaction -often referred to as the smash factor [1,4,5]. the resulting ball flight distance is the main determinant of the success for winning tournaments and the amount of earnings in Professional Golfers' Association (PGA) golfers [6]. Additionally, the club head speed strongly correlates with the players' performance (handicap) [7,8] and in many cases is used as the sole indicator of performance [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…the ball flight results and the impact factors influence amateur players' handicap level, where a lower handicap indicates better performance [1,[19][20][21][22]. Professional players are evaluated by the amount of earnings [6,[23][24][25]. However, performance in both categories can be assessed by the average number of strokes or number of strokes for a single round [26].…”

Section: Introductionmentioning

confidence: 99%

An assessment of the relationships between ball flight results, impact factors, and golf performance

Brožka¹,

Gryc²,

Miřátský³

et al. 2021

Hum Mov.

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Purpose. the aim of the present study was to investigate the relationship between golf players' performance, impact factors, and ball flight results with individual clubs when performing a full swing. Methods. the study involved 9 amateur golfers who performed a full swing test with 4 different clubs (short, middle, long iron, and driver) and then played a tournament round of golf. the players' performance was assessed by handicap and by game statistics from the tournament. the ball flight results were based on the resulting carry distance and the resulting side deviation from the target line. the impact factors were the initial ball speed, the club head speed, the smash factor, the face angle, the club path, and the face to path. Relationships between the players' performance, impact factors, and ball flight results were investigated. Results. A significant relationship was found between the resulting ball flight distance and handicap for long iron (r = -0.85; p < 0.01) and driver (r = -0.9; p < 0.01). Conversely, the resulting ball flight side deviation from the target line correlated with handicap in short iron (r = 0.78; p = 0.02). Conclusions. ball flight distance of long iron and driver and ball flight side deviation of short iron are key attributes of performance. the results suggest that in order to improve long term performance, players should aim for maximal distance with drivers and long irons (high smash factor) and for maximal accuracy with short irons (low face angle).

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Should sports professionals consider their adversary’s strategy? A case study of match play in golf

Wajge,

Stauffer

2024

Comput Stat

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This study explores strategic considerations in professional golf’s Match Play format. Leveraging Professional Golfers’ Association Tour data, we investigate the impact of factoring in an adversary’s strategy. Our findings suggest that while slight strategy adjustments can be advantageous in specific scenarios, the overall benefit of considering an opponent’s strategy remains modest. This confirms the common wisdom in golf, reinforcing the recommendation to adhere to optimal stroke-play strategies due to challenges in obtaining precise opponent statistics. The methodology employed here is generic and could offer valuable insights into whether opponents’ performances should also be considered in other two-player or team sports, such as tennis, darts, soccer, volleyball, etc. We hope that this research will pave the way for new avenues of study in these areas.

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Drive for Show and Putt for Dough? Not Anymore

Cited by 9 publications

References 5 publications

Predicting golf scores at the shot level

Predicting golf scores at the shot level

An assessment of the relationships between ball flight results, impact factors, and golf performance

Should sports professionals consider their adversary’s strategy? A case study of match play in golf

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