Hit or Miss: Kinematic Predictors of In-Game Performance In Collegiate Pitching

By Mitchell, Victoria & Lydon, Will & Van Ness, Mark & Mayberry, John & Rossi, Joey & Jensen, Courtney

June 1, 2018

Hit or Miss: Kinematic Predictors of In-Game Performance In Collegiate Pitching

Mitchell, Victoria & Lydon, Will & Van Ness, Mark & Mayberry, John & Rossi, Joey & Jensen, Courtney. (2018). Hit Or Miss: Kinematic Predictors Of In-game Performance In Collegiate Pitching. Medicine & Science in Sports & Exercise. 50. 664. 10.1249/01.mss.0000538191.10294.d2.

KEY TAKEAWAYS

Predictors of Performance

  • Improve (⇧) Winning Percentage (WP): ⇧ LOAD, ⇩ EXPLODE, ⇩ DRIVE
  • Improve (⇩) Earned Run Average (ERA): ⇧ LOAD, ⇩ EXPLODE
  • Improve (⇩) Field Independent Pitching (FIP): ⇧ LOAD, ⇩ EXPLODE, ⇧ DRIVE
  • Improve (⇧) Strikeouts Per Inning (SPI): ⇧ LOAD, ⇩ EXPLODE

 

POPULATION: Kinetic data from vertical jump assessment as well as in-game performance data were recorded for 30 Division I pitchers during a 4 year span.

SUMMARY

The questions covered:

  • Can in-game baseball pitching performance metrics be predicted utilizing kinetic data from a vertical jump?

 

This study investigates the relationship between in-game pitching performance metrics and data collected utilizing the Sparta Scan vertical jump assessment. Four years of in-game pitching data were collected to determine pitching performance. The metrics utilized were Winning Percentage (WP), Earned Run Average (ERA), Field Independent Pitching (FIP), and Strikeouts Per Inning (SPI). Pitchers were tested using the Sparta Science platform to collect 4 variables: LOAD, EXPLODE, DRIVE, and Jump Height. Multiple linear regression tested the effect of the Sparta Scan data on WP, ERA, FIP, and SPI.

 

  1. Predictors of WP: LOAD (+), EXPLODE (-), DRIVE (-); overall model was significant.
  2. Predictors of ERA: LOAD (-), EXPLODE (+); overall model was significant.
  3. Predictors of FIP: LOAD (-), EXPLODE (+), DRIVE (-); overall model was significant.
  4. Predictors of SPI: LOAD (+), EXPLODE (-); overall model was significant.

 

As improvements in analytics technology become more accessible, these data are able to be analyzed as predictors of performance in sport. The kinetic data taken from the Sparta Science platform correlates with on-field performance. LOAD has a positive influence on all in-game metrics (understanding that lower FIP & ERA are “better”) while EXPLODE has a negative influence. DRIVE has a positive influence on FIP, a negative influence on WP, and no influence on the other two metrics. Recommendations are for future analysis on larger pools of pitchers.

 

Download the PDF to read the full paper here

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