Evidence map›Paper›PMID 41426271›Full record

ArticleOrthopaedic journal of sports medicine2025

Using Pitch-Tracking Metrics to Identify Warning Signs Immediately Prior to Acute Ulnar Collateral Ligament Injuries in Major League Baseball Players.

Morgan R Dillon, Michael A Mastroianni, Nicholas Frappa, Kristen Nicholson, Matthew R LeVasseur, Andrew J Luzzi, Frank J Alexander, Robert Ablove, Christopher S Ahmad

Abstract read
In one paragraph

Article in Orthopaedic journal of sports medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Morgan R DillonDepartment of Orthopaedics & Sports Medicine, University at Buffalo Medical Doctors, Buffalo, New York, USA.ORCID https://orcid.org/0009-0009-5032-316X
Michael A MastroianniDepartment of Orthopedics, Columbia University Medical Center, New York, New York, USA.ORCID https://orcid.org/0000-0001-8332-5131
Nicholas FrappaDepartment of Orthopaedics & Sports Medicine, University at Buffalo Medical Doctors, Buffalo, New York, USA.ORCID https://orcid.org/0009-0007-0623-2864
Kristen NicholsonDepartment of Orthopaedic Surgery and Rehabilitation, Wake Forest Winston-Salem, Winston-Salem, North Carolina, USA.ORCID https://orcid.org/0000-0002-7144-5715
Matthew R LeVasseurDepartment of Orthopedics, Columbia University Medical Center, New York, New York, USA.
Andrew J LuzziDepartment of Orthopedics, Columbia University Medical Center, New York, New York, USA.ORCID https://orcid.org/0000-0001-9714-3873
Frank J AlexanderDepartment of Orthopedics, Columbia University Medical Center, New York, New York, USA.
Robert AbloveDepartment of Orthopaedics & Sports Medicine, University at Buffalo Medical Doctors, Buffalo, New York, USA.
Christopher S AhmadDepartment of Orthopedics, Columbia University Medical Center, New York, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: While ulnar collateral ligament (UCL) injuries have been widely studied, no investigations have characterized the kinematics of acutely presenting UCL injuries or examined the pitch-by-pitch sequence leading up to ligamentous failure. Purpose: To use Statcast pitch-tracking metrics from Major League Baseball (MLB) games to identify potential warning signs for acute UCL injury. Study Design: Retrospective case-control; Level of evidence, 3. Methods: Seven MLB pitchers who sustained acute UCL injuries on a single pitch between 2020 and 2025 were retrospectively identified. Statcast data from the injury game and 4 prior outings were analyzed for each player. Five pitch-tracking metrics (velocity, spin rate, release extension, arm angle, and acceleration magnitude) were extracted for each pitch. For each pitcher and pitch type, a principal component model was trained on control outings; Mahalanobis distance (MD) was calculated to quantify multivariate deviation from baseline. To assess short-term variability, MD values from the 5 fastballs before injury were summed and compared with control-derived thresholds. Pitch-to-pitch trends in MD and individual features were analyzed for progressive changes. Matched controls were selected according to age, handedness, starting versus relief pitcher, and season pitch count. The same process was then completed for a 5-game sample window of pitches for these matched controls. Outlier pitches and periods of elevated short-term variability were identified when MD values exceeded a 95th percentile threshold derived from each pitcher's control data; statistical significance for performance trends was set at Results: Principal component analysis and MD analysis revealed 3 principal findings: (1) every injury pitch was a mechanical outlier based on MD exceeding the 95th percentile of the control distribution (all Conclusion: This study demonstrated that acute UCL injuries in MLB pitchers are often preceded by identifiable changes in key pitch-tracking metrics. Rather than a prolonged deterioration across outings, acute UCL failure may reflect a distinct biomechanical pattern marked by short-term variability and abrupt decompensation within the injury game. These findings provide the first pitch-level characterization of UCL failure and may support future in-game risk-monitoring strategies. However, future studies are needed to help understand these warning signs and prevent UCL injury.

Indexed as

baseballelbowinjury preventionkinematicspitchingulnar collateral ligament

Identifiers

PMID41426271
PMCPMC12717397

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.