Sports Injury Predictor was built to predict which NFL players would get injured in a season

You win in Fantasy Football when you avoid over valued players and find under valued players.

Most fantasy football player valuations do not take the risk of injury into account and are based on:

  • Previous performance
  • Coaching system
  • Competition within the team at that position
  • Ability
  • Strength of schedule
  • Analyst opinion
  • Availability of other players at that position

Although these players were drafted early, the SIP algorithm identified them as having very high chances of being injured. Both missed a large portion of the season due to injury.

Andre Ellington injury history

Andre Ellington
(High Risk)

  • ADP RB 11
  • Produced as RB 21
  • Suffered injuries to his ankle and groin
Julio Jones

Jeremy Maclin
(Low Risk)

  • ADP WR 26 (due to injury concerns)
  • Produced as WR 8
  • No injuries in the season

With no quantifiable way to predict injury, the variable of risk could not be factored into a player’s outlook for the year…until now

Sports Injury Predictor is a patent pending algorithm that determines the probability of a player being injured in a season

The algorithm learns over time and is based on the following factors:

Injury Database

An account of every injury that has taken place to skill position players in the NFL and college for the last 10 years. Includes type of injury, games missed, surgery required and more.

Injury Correlation

Sports Injury Predictor uses an injury correlation matrix to determine the statistical probability of an injury occurring based on previous injury.

Biometrics Data

Age, height, weight are all considered as part of a player’s overall analysis.

Play by Play Data

Position, how many times players will touch the ball in a game, number of plays a player is on the field for all become important in working out the likihood of injury.

We divide the pool of players into high, medium and low risk

By quantifying injury probability we are immune from sentiment that swirls around players as to whether they are “injury prone” or not.

High Risk Players (70-99% probability)

Andre Ellington

Victor Cruz

Julius Thomas

Kyle Rudolph

Low Risk “Safe” Players (0-29% probability)

Marshawn Lynch

Tony Romo

Jeremy Maclin

Lamar Miller

We saw a bump in accuracy in 2014 due to several optimizations we made to the algorithm

High Risk - Top 30% (70 - 99.9%)

75%

Were Injured

Low Risk - Bottom 30% (0.1 - 29.99%)

31%

Were Injured

Search for data on your players now

Player Search

Anticipate injuries to NFL players before they even happen with statistical probability for every player in the NFL.

Compare Players

Compare the injury risk of players head to head.

Search by Position

Search which players in each position are high risk, and which are low risk “safe” players.