The Sports Metrics Lab launched on MLB's Opening Day 2021.
It was created to address the need for more objective statistical analysis in sports. This need was particularly apparent at the mid-major collegiate level, such as the Mountain West Conference, for fans and media outlets such as USA Today's Mountain West Wire, The Herald Journal, The Utah Statesman, and The Statesman Sports Desk, as well as independent sports writers. The Sports Metrics Lab strives to serve those parties clean, objective, and high-quality sports data.
Because the Sports Metrics Lab was created, in part, with a strong focus on the mid-major collegiate scene, particularly the Mountain West, it primarily analyzes that sector, again, particularly the Mountain West. However, the original statistical measures created by the Sports Metrics Lab, and Parker Ballantyne, are practically universal and can be used to analyze nearly any sport, league, or season. In fact, in order to test and prove the viability of those original metrics, they were immediately applied to Major League Baseball and retroactively applied to previous Mountain West Conference football and basketball seasons. The Sports Metrics Lab hopes to continue to analyze more data and to grow its offerings.
Other leagues and seasons can be analyzed and published at SportsMetricsLab.com by request.
The Sports Metrics Lab has curated a number of original and exclusive statistical metrics. Some of which are displayed at SportsMetricsLab.com.
These include:
To see the Sports Metrics Lab original statistical measures in action, select the league or organization to see current rankings and additional information.
DISCLAIMER: Although the DTRS is available during the season, it not a predictive metric. It is purely analytical in nature.
The Dynamic Transitive Record Summary (DTRS) is the central equation of the Sports Metrics Lab.The DTRS is a simple and intuitive metric used to ranks teams and to build other metrics. Nearly every metric that the Sports Metrics Lab uses and publishes is built on the DTRS.
The DTRS was formerly known as the Transitive Head-to-head Conglomerate (THC) but as the algorithm was improved, the name was updated to match the sophistication of the equation and better represent the goals and abilities of the rating system. It is a self-correcting formula that intuitively rates teams based on a team's schedule and results compared with the DRA scores of individual opponents. Analyzing a team's actual performance rather than subjective analysis or an emphasis on a biased category of irrelevant statistics allows the DTRS system to objectively and accurately analyze and evaluate all teams competing within a pool. The DTRS is calibrated to analyze individual teams by tracking a group of teams as they play one another. The DTRS is self correcting and gets more accurate as more data becomes available. It is designed for groups, large or small, that play a common field and at least requires that teams have a large number of common opponents. The DTRS is the basis for a few valuable indexes at the Sports Metrics Lab. The first is the DTRS score itself, and the others are the Static Transitive Record Summary, Compounded Season Aggregate, and the Season Outcome Analysis which are all based on the DTRS score.
The DTRS system is loosely based on the Elo rating system which was developed by Arpad Elo to calculate skill levels for chess players.
DTRS as a predictive indicator for success:
The Sports Metrics Lab explicitly maintains that the DTRS is not indented to be used as predictive metric, particularly for uses in sports gambling. However, despite the DTRS not being designed or approved for predictive capabilities, the the expected DTRS result does have a strong correlation with actual results.
The Static Transitive Record Summary (STRS), also known as Adjusted DTRS, is a companion statistic to the DTRS. Where the DTRS is dynamic, the STRS is static. Rather that continually changing throughout the season, the STRS uses the DTRS results as a base value for teams and constantly refers back to this static base value for evaluation. So, instead of dynamically analyzing each team at the time of a specific matchup, the STRS analyzed a teams performance against an opponent's eventual results.
The STRS also adjusts for the uneven schedules at the time of a given matchup. Because the STRS is only capable of ranking teams in a pool, only in-conference games are considered. Because of the way scheduling works out, teams in any given matchup are not guaranteed to have played the same number of conference games. This means one team's score may be more incomplete at the time of the matchup.
The Compounded Season Aggregate (CSA), also known as Layered DTRS or Compounded DTRS, is the flagship metric of the Sports Metrics Lab. In order to stay completely objective and accurate the CSA uses post season ratings to establish preseason expectations. The DTRS operates under the assumption that all teams start equal whereas in CSA that assumption is adjusted to actual results. So, in CSA a teams' final DTRS result is retroactively factored into a team's base score, then a similar equation is applied to the schedule and results.
The Season Outcome Analysis (SOA) is a metric to determine if a season has a positive or negative outcome. The SOA uses the CSA scale to measure a teams performance from its base value against the average. A score above .500 generally indicates a a positive, or successful, season while a score below .500 generally indicates a negative, or unsuccessful, season. Much like the Mendoza Line in baseball, .300 represents a line of mediocrity. Anything below this line would be considered very negative.
Volatility is a measure of the range of a teams performance and how sporadic or unpredictable a teams results were throughout the season. Volatility is built on the CSA metric and is calculated by taking the range of a teams CSA scores and putting that number into an equation to determine how volatile a team was.
Select the league or organization to see current rankings and additional information.
The DTRS rating system is based on the best available metrics: wins and losses. Using the tried and true inputs, the DTRS gets cleaner, more operational outputs. The DTRS operates under the assumption that not all wins are equal. It weighs wins and losses according to a value system. Without unnecessary metrics weighed into the algorithm, the DTRS is able to produce less noisy data and give an accurate numerical representation of a teams ability to do the one thing that matters: win games.
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For more sports news, stories, and analytics, check out this list of articles! Also visit The Utah Statesman and USA Today's Mountain West Wire for even more coverage and analysis!
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