Though the MLB season is already about a third of the way finished, there was a pretty significant length of time this summer where there was no baseball to be watched. Sure, there was KBO on ESPN, but for people on the East Coast such as myself, these games took place at less than convenient viewing hours. To combat this lack of baseball, I played a lot of Out of the Park Baseball (OOTP), along with growing my own baseball knowledge and understanding by reading, watching videos on YouTube, and perusing databases like Baseball Reference, Fangraphs, and Baseball Savant. It was this combination of activities that gave me the inspiration for this two-part article.
I decided that I wanted to see how teams composed exclusively of some of the best position players in baseball would fare against each other over the course of one or several full MLB seasons. Luckily, using OOTP, I was able to make this a (simulated) reality. Today I will outline the parameters of the simulation and discuss the first season, 2020.
To determine which players would have teams assigned to them, I used Fangraphs and Baseball Reference’s Stathead to determine the 30 best performers since 2017 based on Wins above Replacement (WAR). Unfortunately, because of differences in the calculation methods used by each site, there was not a consensus on the Top 30 players. I decided to calculate both the sums and averages of the different WAR totals for each player for a simple method of figuring out who was the best based on both measures. As I suspected, the rankings for the sums and averages for each player gave me a definitive Top 30 players, as seen below. Players who appeared in the Top 30 according to only one site appear to the right of the red line, and are listed by the sum total of their two WARs.
Since these teams are made up exclusively of position players, I had to decide what to do about pitching in this simulation. I definitely wanted to have an identical pitching staff on each team, but was not sure how it would be composed. I remembered a video I came across on YouTube by Outta Here Baseball, where they use various statistical methods to determine the most average MLB pitchers, one starter and one reliever. In this video, they used both the 2019 season and combined statistical totals from 2017-2019 for their evaluations. This video is also what determined the time frame that would be used to determine which players to use, as I was originally torn between using three years or five years. Based on Outta Here Baseball’s conclusions, I chose Jon Lester to make up the starting rotation, and Kelvin Herrera to man the ‘pen.
After firing up OOTP 21 and starting a new standard game, I set about creating my teams. Using commissioner mode, which allows a player to control all aspects of the in-game league, I released all players off of all teams, and deleted all except the 32 players that are relevant to this simulation. I fired all coaches, turned off all AI control of rosters and strategies, and set all 30 strategies to “Balanced.” Also, injuries, suspensions, and player development and aging were disabled to try to control as much as possible for variation in playing time and ability across all teams, and all parks were set to have neutral factors. The league will be using the universal designated hitter rule, because, frankly, I don’t want Jon Lester hitting. I did everything I could think of to ensure that the only variables present in this league would be the varying abilities of the players involved.
Using a random number generator, each player was assigned a team they would replace in MLB, as seen here. Each team consists of 26 players: three catchers, one player at each infield and outfield position, one utility/designated hitter, one infield utilityman, one outfield utility man, five Jon Lesters, and eight Kelvin Herreras. For left-handed throwing players, I changed them to right-handed throwers for positions such as 3B, SS, 2B, and C, because making a lefty play these positions could have an even more dramatic impact on the league than just a righty playing out of position already will have. The depth charts and lineups for each team are identical.
The players in this league represent OOTP’s version of what they are in 2020. Therefore, some of the older players, such as Joey Votto, will almost certainly be at a bit of a disadvantage in this simulation. In the same vein, I expect that the defense in this league will be absolutely atrocious. One of the most important defensive positions on the diamond is the catcher. There is only one catcher featured on this list. I expect this to result in a very high number of passed balls, wild pitches, and potentially even a high number of stolen bases. The first basemen and the more defensively-challenged corner outfielders on this list will also be expected to struggle greatly, especially at positions such as CF and SS, and will cost their teams a large number of runs by virtue of not having the range and defensive ability to play those positions. I almost feel bad for subjecting so many virtual Lesters and Herreras to this experiment, as we could see some incredibly high ERAs, potentially into the teens.
At the end of this experiment of, say, three seasons, I would expect that the teams featuring more versatile or “athletic” players (think Mookie Betts and Cody Bellinger) will be more successful than those featuring corner infielders. Of course, Mike Trout is a serious contender too, because, you know, he’s Mike Trout. If I had to choose one team to finish with the highest winning percentage by the end of this experiment, it would be Bellinger’s Marlins. He’s one of the most electric young players in today’s game, and one of the best hitters on the list. Additionally, his defensive versatility could lead to him giving up fewer runs at more premium defensive positions than some of the other players in this experiment.
Season 1: 2020
The preseason predictions for 2020 largely show what I expected. Six teams in the league are projected to finish with over 100 wins, including the aforementioned Bellinger and Trout, as well as Aaron Judge, Alex Bregman, and others. It also indicates that this league could possibly be even more high-scoring than I originally anticipated, as there were teams scoring thousands of runs, and players hitting over 100 home runs. They are just predictions generated by the simulation engine, but I am interested to see how accurate they prove to be.
At the end of the 2020 season, it is obvious that my expectations for high offense were certainly well-founded. In fact, I significantly underestimated. The WORST offensive team, Andrelton Simmons’ Nationals, slashed .377/.461/.521 and scored almost 1800 runs. The team combined for -36.2 of OOTP’s batting war, but still won 74 games thanks to being the best pitching team in the league, due to Andrelton Simmons being an absolute defensive wizard.
On the other end of the spectrum, the Trout Cubs were so ridiculous that I can hardly even put it into words. Take a look at the batting stats for the Cubs:
Every starter on this team except the catcher had triple digit home runs and over 13 WAR on the way to a 120-42 record. You know what they say, just Trout being Trout. The Millville Meteor finally gets his first ring as the Cubs beat Francisco Lindor’s Indians in 7 games in the World Series. Bellinger, Betts’ Orioles, Bregman’s Padres, and Marcus Semien’s Astros are division winners, each winning more than 105 games. The Votto Rangers indeed struggled immensely, winning only 31 games thanks to the worst zone rating and most errors in the league. All nine starting Mike Trouts finish in the Top 9 for NL MVP Voting, with Mike Trout RF taking the hardware, while Giancarlo Stanton CF snags the AL award with a 1.553 OPS, 138 HR, and 392 RBI, but only a 4.5 WAR.
Overall, Season 1 of the experiment largely reflected my expectations, and I imagine that Seasons 2 and 3 will have similar statistical results. The big question, however, is who will come out on top at the end of the three seasons. Stay tuned for Part 2 in the coming days for the 2021 and 2022 seasons, and final conclusions from the experiment!
Featured image via Keith Allison