Not a lot of kids know what they want to do with their lives while they’re still in high school. I met Jacob Mox at a Minnesota Gophers’ basketball game in the press box, and he’s got a pretty good sense of his career path.
Using my standard rule – if you meet someone young, suggest that he’s older; and if you meet someone older, suggest that he’s younger – I asked youthful-looking Jacob if he was just out of college. Jacob tactfully let me know that he was actually still in high school.
The Eagan, Minnesota resident went on to tell me a bit about his sports statistics company that he’s started with some friends. It sounded interesting especially since at Jacob’s age, I was bagging groceries at Red Owl. Heck, Jacob was even coming up with his own baseball stats to evaluate players.
But, I’m jumping ahead. Here’s Jacob Mox’s story in his own words.
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High School Friends Start Moneyball Company
By Jacob Mox
In the modern era of sports, fans have debated hundreds upon hundreds of dilemmas. Debates that include “Who’s the best baseball player in history”, LeBron vs. Jordan, or “Who is history’s most clutch batter?” Is it better to bunt or hit away with no outs and a runner on first? In the sports world, much like the business world, arguments are supported by facts and statistics.
Growth of Sabermetrics
In baseball, Sabermetrics or Moneyball, was created to answer these very questions. Sabermetrics is defined as, “the application of statistical analysis to baseball records, especially in order to evaluate and compare the performance of individual players.” Statistical pioneers like Bill James and Davey Johnson set the foundations for Sabermetrics in the 1970s, and its use has expanded greatly ever since.
The significant growth of Sabermetrics has forced practically every single professional sports team to rethink how the game is played. Baseball is without a doubt the most evident example of this. Because it is not as fluid as sports with continuous plays like basketball or hockey, it is much easier to value each and every action that takes place on the field. Sabermetrics uses this to quantify practically anything that could ever happen in a game. Examples of the new age statistics include defensive route efficiency, catcher’s efficiency, and even being able to determine how much a player’s home stadium helps or hurts their traditional stats.
There are two major problems when new waves of statistics are developed. The first is that people in positions of power are typically skeptical of the merits of Sabermetrics. In a sport as traditional as baseball, it is not uncommon for front offices to be reluctant to implement new strategies. Scouts and statistics often go head to head over which players have “the stuff”, and GMs and statisticians say very different things about who should be paid how much. The other major problem is that it takes time for the statistics to be fine-tuned. Because the game changes from year to year, the statisticians are forced to determine what factors have changed and in what ways the stats need to reflect those changes.
Baseball Cards and Moneyball
In the fall of my freshman year of High School, I came face to face with these very same problems. My whole life, I have been fascinated by sports statistics. I collected baseball cards from a very young age, and liked to read through the stats on their backs whenever I had nothing else to do. Outside of a strong distaste for RBI’s, I didn’t get into the philosophical questions about baseball statistics until I was in 8th grade. That coincided with the first time I watched Moneyball starring Brad Pitt. After I watched that movie I was hooked. I watched it dozens of times since then, but that wasn’t enough. I was so intrigued by the core concepts covered in the movie but I knew that there had to be so much more than what was just on the surface. I went to libraries and checked out books written by Baseball Prospectus writers, a publication that covers baseball statistics, and read them all cover to cover.
Developing Our Own Baseball Stat
By the start of my freshman year in High School I had a notebook full of notes on various metrics I had read about, and ideas for statistics that I could develop on my own. Like the founders of Sabermetrics, it was a question that finally got me on track to where I wanted to go. On a car ride home from a Minnesota Twins game with a friend, he and I were debating how to properly value a player. The one thing we were stuck on is how to deal with RBIs. For those of you who may not have thought about it this way, RBI are a misrepresentation of how many runs a player actually earned. Situations like a bases loaded walk, where little or no work needs to be done by the batter, count as an RBI. RBI also change based on batting order. If player x bats after a player who gets on base 50% of the time, and player y bats after a person who gets on base 40% of the time, player x has a 10% higher chance of even having a chance to get an RBI. So as we asked ourselves how to deal with the problem, I did some research and stumbled across the most influential piece of information that I have seen since I started developing my own stats: Run Expectancy Matrices.
Now, I’m not going to tell you that REM’s are the most exciting things in the world, but they are by far the most useful things in all of baseball statistics. Essentially, an REM is a table with every single combination of outs and base runners on the axes, and the chart is filled in with the amount of runs the average team is expected to score with that situation. This killed two birds with one stone. Not only could we scrap RBI evaluation for individual players, we could also compare a team’s actual runs to it’s expected runs to see whether they are above or below average efficiency. Throughout a painstaking process of going through play-by-play of 30 MLB games, one for each team, I was able to conclude that our method was accurate to an exceptional degree. It was slightly below the accuracy of WAR, the most prevalent statistic currently used to measure overall production. This first step was a long and difficult process, but it didn’t get any easier once we moved on from our initial work.
Having spent an immense amount of time creating the stat, I was able to convince somebody within the struggling Twins’ organization to hear my pitch about how useful the statistic could be. The only problem was that the Twins were one of the last teams to put their faith in ‘modern baseball statistics’. Twins officials were dismissive of the work I had done and told me that they would rely on the traditional metrics and ‘eye-test’ scouting. Since that day, I have made it my goal to work hard on my statistics because I know that there are still many professional teams who have yet to make the switch to more modern and accurate statistics, as well as amateur teams who don’t have the money to pay for a traditional sports statistician.
I have put in countless hours scrawling notes in notebooks, doing monotonous data entry into spreadsheets, and tireless research in an attempt to further improve my stats to the best of my ability.
A Company is Born: Minnesota Sports Statistics Analysis
This brings me to today, and the foundation of our company. Following the advice of a Dean at Drake University, I created Minnesota Sports Statistics Analysis as a way to showcase all the hard work I have done in the field of statistics, as well as spreading my knowledge to people who might be in a position like I was 5 years ago. Since we started as a way to get word out about our work, we have grown into a fully operational sports media company that does writing, photography, as well as offering statistical consulting to sports teams of any level. Our mission statement is “To give all levels of athletic teams access to comprehensible statistics, in order to help them achieve more.”
We have expanded into basketball statistics and have been covering the Minnesota Gophers Men’s Basketball team this past winter. My friend Jerry Ostrem and I have split time as the site’s photographer, and my friend Steven Wagner has been working hard to code and design a new website, which should be launched in the next several weeks. You can visit our current website at mnssa.blogspot.com. We also have been given the opportunity to cover the Minnesota Twins during the current season, marking our first time covering a professional team.
Although it hasn’t been an easy task so far, I am excited to see where my future in sports statistics will take me. I will be following a statistics based path in Actuarial Science at Drake University. Even with being a little further away from where it all started, Minnesota Sports Statistics Analysis will not stop.
Jacob and his company can be reached via Twitter at @mnstatsco.