# Team of Numbers

Baseball has been described as a game of numbers for years, the numbers that are involved in baseball are used in sabermetrics. Major League Baseball teams are set on sabermetrics to help determine what players will fit their team best and give them the greatest opportunity to win. Some teams have done well using sabermetrics, while others are still at the bottom of their divisions. Sabermetrics is a vital part in scouting and looking for players to help your team win games, but will not result directly in wins or championships.

Teams have had success while using sabermetrics, some of these teams include the Tampa Bay Rays, and Boston Red Sox. Their records have dramatically increased, and they have become two power house teams in the American League, also making the American East division one of the strongest in all of baseball. Players that the Red Sox have taken from the use of sabermetrics have included Dustin Pedroia, Clay Bucholtz, Jonathon Papelbon, and Jacoby Ellsbury (Simmons,1). Sabermetrics were used on a player like Jacoby Ellsbury, and some of the statistics that caught the eyes of the Red Sox organization were fielding percentage ( amount of putouts divided by the amount of total chances the player has) and also on base percentage (the percentage that the player will get on base per at bat). These two statistics have been useful for the Red Sox and Ellsbury has proved that those two statistics are a big part of his game. In his first three years in the major leagues Ellsbury had on base percentages of .394, .336, and .355 which are great, his fielding percentage has also been a 1.000 (which is perfect) in 2007, 2008, 2010, and 2011. The Rays on the other hand were trying to just rebuild their whole team around young talent, some of which included Carl Crawford, Rocco Baldelli, B.J. Upton, and Scott Kazmir.

Not all teams have success with their sabermetrics, almost all teams use sabermetrics as a main tool in drafting and getting players in the offseason and during trades. Some teams pick players and they just do not produce how they were predicted to and also they may just not produce in the major league. Throughout the major league a system called PECOTA (Player Empirical Comparison and Optimization Test Algorithm) has been used to determine project how many wins a team will have based on 3 factors Major-league equivalencies to allow us to use minor-league stats to project how a player will perform in the majors, baseline forecasts which use weighted averages and regression to the mean to produce an estimate of a player’s true talent level, and a career-path adjustment which incorporates information about how comparable players’ stats changed over time (Swartz, 1). PECOTA has projected wins for every team, and it showed that teams were either over projected or under projected. Three examples for teams that were over projected would be the Pittsburg Pirates, Cleveland Indians, and Arizona Diamondbacks. The Pittsburg Pirates were projected to have 72.4 wins but actually had 57 wins, the Indians were projected to have 87.2 wins but actually had 69 wins, and the Arizona Diamondback were projected to have 84.4 wins but had 65 wins (Swartz, 1). This proves that plugging statistics into a computer and letting the computer determine how many wins a team will have is not the best way to determine if the team will be successful that season or not.

There have also been players that have been projected by sabermetrics to not have good seasons, and they have had amazing seasons. One such player is Jose Bautista of the Toronto Blue Jays. He has played in the major league and minor leagues since 2005, but even before that his minor league statistics showed him to be a pretty good hitter not very many homeruns, and a low slugging percentage. Going into the 2010 season that was still what was being said about him until he had 54 homeruns that year and a slugging percentage of .617, then in the 2011 season he hit 43 homeruns with a .608 slugging percentage. That is unheard of, he all of a sudden just got so much power, and started hitting the most homeruns in the major leagues. This is an example of how the PECOTA is not an effective way of determining players because it gave the number of homeruns that Bautista would have and it said he would have 18, not 54 or 43.

Statistics are a big part of baseball, but it is something that is more effective after the games and developing after seasons. Before a season it is not the best thing to determine the effectiveness of players and their season averages. Players have some things that statistics cannot calculate those things are experience, heart, and streaks. Championship teams usually go on streaks towards the end of the season and they start winning all their games, also all the players are playing at their best. Statistics cannot calculate if a team is going to go on a hot streak towards the end of the season or on a cold streak. Heart is one thing that can never be measured by a statistic and can determine if a team will win games and maybe even a championship. Players that have a lot of heart will do anything to win, they will do whatever the team needs them to do to win games. Experience is another thing that cannot be measured by statistics and that can prove to be a big part of winning games. Experienced players can provide guidance to younger players and they have been in the major leagues for years. These things can provide a better chance of winning games than numbers from years prior. Sabermetrics will be a part of scouting because it has been around for years, but there is no guarantee that it will help in winning games or getting players to help win a championship

Works Cited

“Jose Bautista Stats.” *Toronto Blue Jays*. 2011. Web. 11 Apr. 2012.

Simmons, Janet. “Eyeballing the Sabermetics game.” *ESPN*. ESPN Internet Ventures, 06 Mar. 2012. Web. 11 Apr. 2012.

Swartz, Matt. “Ahead in the Count.” *Baseball Prospectus*. 03 Sept. 2010. Web. 11 Apr.

This is good work, Eddie. It might almost be called a non-causal essay because, while it investigates a cause-and-effect proposition, it concludes that the effect can’t often be predicted from what seems to be the cause.

It’s not surprising that predictions are easier for individual players than for whole teams. While players can have ups and downs, so many variables contribute to team success it’s impossible to factor them all.

The sabermetric measurements you cite here are pretty straightforward, wouldn’t you say? OBP and fielding percentage are so common now I guess we should conclude we all take sabermetrics for granted.

There’s probably a good statistic for “warning-track” hits, don’t you think? If Bautista had a strong WTH number for several years in a row, it should have been possible to predict he could hit more bombs if he got a little bit stronger. Maybe that’s all it took. Or hypnosis. Or one of those silly red and white rope necklaces.

Good work.

Grade recorded.