Category: Analytics blog

Reel Analytics’ studies show that past performance can be a strong indicator of future success at the highest level of the sport

Leveraging the power of computer vision technology, RA found that the requisite traits found in NFL players can be identified as early as high school. Computer vision technology enables evaluators to analyze the high school tracking data of successful players from the past to detect the trait(s) that made him ‘special’. This type of retrospective analysis allows college coaches to establish position-specific performance thresholds used to evaluate high school prospects, and to serve as early indicators of future success. For NFL coaches and personnel executives, high school tracking data, among many things, provides deeper insights into the progression of a draft prospect’s raw and functional athleticism.

Reel Analytics’ evaluation methodology consists of two main factors: an In-Game Athleticism Score (IGA Score) and a Production Score (PROD Score), which are both used to determine the RA Score that serves as the final overall player grade. Reel Analytics’ IGA Score derives from objective evaluative metrics extracted by their AI-powered tracking technology, and Reel Analytics’ PROD Score derives from objective advanced performance metrics.

The findings from Reel Analytics’ retrospective study of nearly 500 players from 2011 to 2020 show that RA’s scoring models’ overall projection accuracy is 75.5%, a hit-rate that is 3X higher than college football’s average, and more than 2X vs.the NFL average.”

Discover the top 1% of propsects with elite in-game athleticism

An athlete with rare traits (innate athletic ability, functional speed, functional strength, raw power, instincts and functional size) can be defined as a ‘freak athlete’. These high-ceiling, potentially high-impact athletes, can offer teams a significant matchup advantage that contribute to wins if the talent is developed. Freak athletes aren’t always best players or major contributors on a team, in part because traditional athletic metrics (40-yard dash, short-shuttle, 3-cone, standing broad jump, vertical jump, etc.) aren’t measured in a game environment.

By measuring the raw functional athleticism of successful NFL players in a game environment, Reel Analytics has redefined the traits found in ‘freak athletes’. As a result of this data, our clients have access to our exclusive Reel Freak List, which features the top 1% of prospects with elite in-game athleticism. Here are the athletes who made the list in 2023.

Balancing the art and science of player evaluation for improved hit-rates

Football coaches have a saying that is indicative of their evaluation philosophy, “the eye in the sky don’t lie”. Most NFL coaches will tell you that player evaluation is 90% what’s seen on film, and 10% combine measurables and interviews. Technological advancements powering tracking data has allowed coaches to quantify the critical factors (athletic ability, functional speed, functional strength, and instincts) they look for when they turn on the tape. Armed with our tracking data, coaches can augment thier film evaluations with our veried in-game athhletcism data.

In most cases (with the exception of the bench press test), our tracking data can measure the same critical factors of athleticism as traditional combine testing. However, our tracking data has a few clear and distinct advantages over combine testing when measuring athleticism:

1. Tracking data measures athleticism in a live environment (e.g. during games) and/or in a semi-live environment (e.g. during practices), making it easier for evaluators to project talent to the next level.
2. Tracking data measures positionspecific athleticism in the context of executing position-required movements repeatedly during a game or practice.
3. Tracking data can be customized to track and measure a specific athletic trait deemed important by a coach or scout.
4. Tracking data can provide trending analysis useful to quantify the impact of age or injury on athletic performance.

Linking tracking data to the fundamental aspects of position, time, velocity and acceleration in the context of physics and mathematics

Our player tracking approach is designed to extract player positioning vs. time data, which then allows the calculation of traditional and emerging player metrics. Athleticism is often defined in terms of speed, explosiveness, burst, and acceleration, with various metrics and various units commonly used. These metrics date to the early 1900’s with the introduction of the Sargent Jump Test, a vertical jump test put forth by Dr. Dudley Sargent. However, more than 100 years later, critical gaps remain in the accuracy and applicability of metrics used to measure athleticism in the context of football.

Download our white paper for more information on the physics behind the measurements and our approach to modern-day athletic measurement.