From the Booth #3: Big Red Advanced Stats

This is the third post in a six post series featuring the Cornell At Bat broadcasting team. These posts will appear throughout the Fall semester. For part one of the series, click here. For part two, click here. 

Here at Cornell At Bat, we are devoted to delivering the Big Red fans with the  latest and most in-depth analysis of the baseball team. This year, analyzing statistics will be at the forefront of my duties with Cornell At Bat. Sabremetrics, or advanced stats, have taken the baseball world by storm over the past few years, especially after Michael Lewis’ book Moneyball and the film that followed it. However, advanced analysis  of NCAA baseball has little to no existence. I thought it would be interesting to preliminarily derive one of the most basic and popular sabre stats for the Big Red: WAR, or Wins Above Replacement.

WAR attempts to assess the overall value of a team’s players compared to the average player (the replacement). Batting WAR attempts to minimize the habit of simply looking at traditional and less valuable stats, such as AVG and RBIs, in an effort to get the most accurate assessment of a player’s talent and value. WAR is at the forefront of analyzing MLB players and their success. This has especially been seen with the debate of the American League MVP, where proponents of Mike Trout receiving the award claim that he has the highest WAR of any player in recent years. Determining WAR for an NCAA team is a lot harder however, for certain statistics that equate to a very precise WAR do not exist. My WAR for the Big Red is a very rough and basic form, as the stat really isn’t developed for anything besides MLB at the moment. Stats like park adjusted stats for average OBP and SLG don’t exist for the NCAA, and neither do other advanced stats that would make the calculations more precise.

My calculations for the Big Red WAR values were accomplished using an online calculator. For the calculator, I needed to input the critical stats for WAR of the average, replacement player (OBP and SLG). I used the averages from the whole Ivy League for the 2012 season for this facet. I then inputted individual stats from the Big Red team last year for all of the other categories, as well as the players’ positions (certain positions have higher values). For fielding and base running, they ask for a scale from best to worst, and I came to conclusions using fielding percentage and steals/ caught stealing percentages. I compared each player’s stats against each other to come up with the scale.

Although not completely precise, these WAR values will still accurately assess the players’ values. Also included is RAR (Runs Above Replacement), which is the sum of all the run components (Batting, Fielding, and Base Running). WAR is derived from RAR by dividing it by a runs per win constant, which is standardly set at 10/game for MLB (I couldn’t change this, but if I eventually calculate the Ivy League’s runs per win, I can redo the computations). Here are the WAR and RAR values for the 2011-2012 Big Red:

Returning members with over 20 games played, in order of most valuable to least:

 Brenton Peters: 1.6 WAR, RAR 15.8

 Chris Cruz: 1.5 WAR, RAR 15.2

 Ben Swinford: 0.6 WAR, RAR 6.1

 Tom D’Alessandro: 0. 6 WAR, RAR 6.0

 JD Whetsel: 0.1 WAR, RAR 1.0

 Kevin Tatum: -0.1 WAR, RAR -0.8

 Matt Hall: -0.2 WAR, RAR -2.0

Departing Starters, in order of most valuable to least:

Brian Billigen: 2.6 WAR, RAR 26.6

Brandon Lee: 1.2 WAR, RAR 12.2

Frank Hager: 1.0 WAR, RAR 10

Marshall Yanzick: 0 WAR, RAR 0.2

For more information about this post, please contact Alex Garcia at asg232@cornell.edu. 

For more information on Cornell At Bat or if you want to get involved, contact Alex Gimenez email at ajg322@cornell.edu. For previous Cornell At Bat adventures, look here, here, here, here, and here.  

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Sportvision and the Future of Sabermetrics

During the 1970s and 1980s, Bill James revolutionized baseball through his collection of Baseball Abstracts. His unique perspective of evaluating players and discovering their true impact on their team's chances of winning was the beginning of a movement that would shake the very foundation of the sport.

Since the last Baseball Abstract was published in 1988, baseball sabermetrics have only continued to become increasingly popular and crucial to the ways franchises construct their teams. They even developed a presence in pop culture through the release of the Michael Lewis’ best-selling novel, and the later Hollywood film, Moneyball.

As sabermetrics have proven, through the success of teams like the Oakland A’s, to be effective in terms of evaluating the value of players, the precise statistics used have continued to evolve. Over the last ten years, sabermetrics have moved from the days of Bill James’ Runs Created, Win Shares and Range Factor statistics to even more complex formulas with more specific aims.

For example, the original Runs Created statistic that was developed by James has now been superseded by Weighted Runs Created plus (wRC+), a new equation which compares a player's On Base Plus Slugging (OPS) against the league average and then accounts for ballpark factors and run-scoring environments.

While sabermetrics have continued to become increasingly refined and specific, all of these detailed new statistics still only evaluate results. These advanced statistics such as Wins Above Replacement (WAR), Expected Fielding Independent Pitching (xFIP) and Skill Interactive ERA (SIERA), as effective as they are, are results-based. They fail to answer the question of why these results occurred.

Introduce Sportvision, a company whose technologically advanced cameras have been placed in Major League Baseball stadiums since 2006. Most fans probably already know Sportvision from the K-Zone cameras featured prominently by TBS and Fox this postseason. While Sportvision cameras might be enhancing the fan experience, their real value lies in the data they collect for teams to analyze.

Sportvision has developed services called Pitch F/X and Hit F/X, which track and record data from every single Major League Baseball game. For example, Pitch F/X tracks the velocity, horizontal movement, vertical movement and location of each pitch thrown. This data allows teams to analyze which pitch was most effective for a given pitcher and why that pitch was effective. A team could also analyze the value of velocity compared to location or movement.

Hit F/X takes a similar approach in analyzing batters. Instead of focusing on the results of each at bat, Hit F/X tracks the contact point, speed of the ball off the bat, elevation angle and field direction of each batted ball.

Sportvision, recognizing the value of this data, has created SCOUTrax, which uses the data from Pitch F/X and Hit F/X, as well as a third creation of theirs, Command F/X, to create heat maps and charts to better display the data to fans.

This new technology has opened the door for an endless number of new ways to evaluate the effectiveness of players, as well as help teams develop their own players. 

For example, teams will be able to see the value in an added half inch of movement to a pitcher’s fastball compared to extra velocity. Or, the team will be able to see that, although a particular hitter might not have had the best statistical year, he actually hit the ball particularly hard a high percentage of the time and should have fared better.

As the Sportvision data continues to be analyzed further, look for the development of future sabermetrics that are completely process-driven. Websites such as www.fangraphs.com have already started developing these types of statistics using Pitch F/X, such as Pitch Type Linear Weights, which attempts to determine a pitcher’s run expectancy per a given type of pitch.

Yet, the data from Sportvision is still in its youthful stages and needs to be further refined. For example, cameras in certain stadiums may read pitcher’s velocity or movement slightly differently, thus altering the way the data matches up. This issue might only create minor variances, but it is essential that it be fixed in the near future so a totally uniform data set can be collected.

Despite these minor deficiencies, Sportvision still holds the potential to greatly affect the way coaches, player personnel directors and baseball operations professionals develop players as their careers progress. As sabermetrics continue to evolve and become more refined, expect the data collected from from Pitch F/X and Hit F/X to be a central focus and have a profound impact on the game.

A Statistical Dissection of the 2012 Oakland A’s: Billy Beane’s Finest Work

Billy Beane 

The Hollywood blockbuster, Moneyball, ended much like any other feel good film, as protagonist Scott Hatteberg belted a home run that sent the small market underdog Oakland A’s into the postseason. However, unlike most silver screen dramas, the story of the Oakland A’s did not end with a “happy ever after” attached at the end.

After using Billy Beane’s innovative scouting to build teams that posted above .500 records from 1999 to 2006, the A’s could not finish above that mark for the next 5 years. It seemed as though the rest of the league had caught on to the A’s strategy, and that Billy Beane had lost his touch. Using their economical advantage, combined with the sabermetric scouting originally developed by the A’s, other larger market teams seemed to once again hold a competitive advantage over Oakland.

At the onset of the 2012 baseball season, virtually all baseball fans could not imagine the Oakland A’s making much of an impact in an AL West division dominated by the superstar heavy Texas Rangers and Los Angeles of Anaheim. However, 2012 turned out to be Billy Beane’s most masterful work yet as the Oakland A’s finished 94-70, winning the division with a payroll of roughly $99 million less than Angels and $65 million less than the Rangers. So how did this happen? How did the A’s once again use undervalued talent to reach the postseason?

When opposing pitchers stared at the Oakland A’s lineup card during 2012 season, it is hard to imagine any of them shaking in fear. The A’s did not feature any hitter batting over .300 or who drove in over 85 runs, and only had three players finish with more than 20 home runs. However, the A’s use of platooning, a declining trend in Major League Baseball, to account for their deficiencies, led to the underrated effectiveness of their lineup.

At first base for example, Brandon Moss is the regular starter against right-handed pitching, while Chris Carter usually gets the start against left-handers. Moss put up stellar numbers against right handed pitching all season, hitting 19 home runs, while driving in 44 runs and slugging .643 in 207 at bats. Against lefties, Carter hit five home runs, drove in 17, while slugging .494 in 83 at bats. Another platoon employed by the A’s is at designated hitter with Johnny Gomes and Seth Smith. Smith homered 12 times while slugging .454 and having an OPS of .805 against right handers in 313 at bats. While Gomes hit 11 homers to go along with a .413 OBP and .974 OPS against left handers in 164 at bats. The A’s have also prominently used other platoons throughout the season, such as at the catcher spot with Derek Norris and George Kottaras.

While the A’s use of platooning did spark their offense, this success did not stand out in traditional statistical categories. With a batting average of .238 the A’s finished 28th in the league, leaving only two last-place teams, the Houston Astros and the Seattle Mariners, with lower marks. Even On Base Percentage, a statistic prominently featured in the original Moneyball, cannot account for their 2012 success, as their .310 OBP ranks 24th in the league.

However, what Oakland lacked in getting on base and batting average they made up for in the power game. The A’s finished 7th in the league in home runs and while their slugging percentage ranked 15th in the league, that ranking is considerably higher than expected given their lowly batting average. In the sabermetric community, Oakland’s hitters excelled, as well. The A’s ranked 10th in the league in Weighted Runs Created (wRC+), an improved version of Bill James’s original Runs Created statistic, which “attempts to quantify a player’s total value and measure it in runs.” Overall, despite a ridiculously low batting average and lack of star power, the A’s were able to produce the 14th most runs in Major League Baseball, which, with their surprisingly impressive pitching staff, proved to win a lot of ballgames.

Similar to the offense, the A’s pitching staff proved that big names are not a necessity for success. Unlike the hitters, however, more traditional metrics can be used to quantify the A’s pitching staff feats. In 2012 the A’s staff posted a 3.48 ERA, which was good for 6th best in the league to go along with 1.24 WHIP. The one anomaly was strikeouts, in which the A’s finished 26th in the league. This lack of strikeouts, however, was not truly an anomaly, but rather an indicator of a general pitching strategy employed by the A's.

The A’s finished 9th in the league in fewest walks allowed going along with having the 10th lowest walk percentage. The A’s also had the 7th highest percentage of fastballs thrown. These statistics show that A’s pitchers used the strategy of attacking the zone with fastballs and forcing contact in order to limit opposing batters. This strategy does not require having pitchers with dominant strikeout arsenals, a skill set generally more expensive to obtain.  While pitching to contact by attacking hitters with fastballs worked in 2012 for the A’s, advanced sabermetrics suggest that the success of the A’s pitchers might be somewhat attributable to luck. The A’s had the third lowest BABIP (Batting average on balls in play) in the league, the 7th highest xFIP (Expected fielding independent pitching), and the 8th highest SIERA (Skill interactive ERA). Having a low BABIP is a signal that many balls hit by opposing batters went directly to Oakland fielders. Going forward, this might not always be the case and more balls could fall in for hits. xFIP is a statistic that attempts to judge pitchers on entirely what they can control, using a combination of strikeouts, walks, hit by pitches, and “how many home runs they should have allowed” (using home run to fly ball ratios and multiplying it by fly ball rate), to try and determine how effective a pitcher is without the effect of his fielders. The higher the xFIP a team or pitcher has, the worse they were and the A’s xFIP of 4.2 is categorized as “below average” by Fangraphs.

The last sabermetric in which the A’s pitching staff failed to excel was SIERA. SIERA differs from xFIP because it places more emphasis on balls in play, using groundball and flyball rates, as well as walks and strikeouts to attempt to determine a pitchers skill. The A’s finished the year with a SIERA of 4.03, which was the 8th highest in all of baseball. So while the A’s did have one of the most effective staffs in the league according to traditional measures like ERA, advanced sabermetrics predict that the A’s success might soon run out or is unlikely to reoccur. In total, however, the A’s only allowed the 6th lowest amount of runs in the league in 2012, which along with their power and timely hitting led to winning 94 games.

The next question one must ask when dissecting the success of the 2012 Oakland A’s is, how did Billy Beane assemble this team? What changes were made from 2011 to 2012 that led to this significant increase in wins?

The only headlining acquisition was the signing of international free agent Yoenis Cespedes for a 4 year, 36 million dollar deal. Cespedes might have been an international superstar, but many in baseball circles thought that it would take some time for him to adjust to Major League pitching. Yet, the A’s Director of Baseball Operations Farhan Zaidi, felt he could be a key contributor and convinced Beane to make the investment. The 2012 season is proof that Zaini made the right judgment, as Cespedes hit .292 with 23 home runs and 82 RBI’s.

While the Cespedes deal made headlines, it was the more low key moves made by Beane that really boosted the A’s all season long. Similar to the 2002 season depicted in Moneyball, Beane was able to find value in players where other teams did not. In a deal with the Red Sox, Beane sent reliever Andrew Bailey and Ryan Sweeney to the Red Sox for inexperienced outfielder Josh Reddick and two minor leaguers. As stated earlier, Reddick exceeded everyone’s expectations by hitting 32 homers and slugging .463. Also, in a multiplayer deal with the Diamondbacks, Beane traded starter Trevor Cahill and veteran reliever Craig Breslow for rookie Jarrod Parker, reliever Ryan Cook, and Colin Cowgill. In 2012, Parker was arguably the best rookie pitcher in the American League posting 3.47 ERA in 181.1 innings, while Ryan Cook was an All-Star reliever in the set-up role with a 2.09 ERA and 42 holds.

The third trade which significantly increased the strength of the team was the deal with the Nationals, which sent Gio Gonzalez and a minor leaguer for rookie Tommy Milone, Derek Norris, Brad Peacock, and a minor leaguer. While Gonzalez won 20 games, he would have soon been a contract the A’s could not afford. The newly acquired Milone, a soft-tossing righty with an average fastball velocity of 87.7 went on to also be one of the best rookie pitchers in the American League, posting a 3.74 ERA while winning 13 games. Norris also stepped in, playing a crucial role at the catcher position as half of the platoon with George Kottaras.  All of these trades represent examples of Beane dumping established players for relatively unproven talent, yet in all three cases the unproven talent was able to significantly contribute the A's success in 2012; this is a great tribute to the effectiveness of the A's scouting department.. These three trades, along with a trade with the Rockies for Seth Smith and the signings of journeymen Jonny Gomes and Brandon Moss, are all examples of Beane’s ability to trade and acquire underutilized cheap talent.

Though the 2002 season will probably be most remembered when people look at the Billy Beane’s career as General Manager, it is the 2012 season that is by far his finest so far. Unlike 2002, in 2012, Beane did not have the luxury of marching out three all-star quality starters (Barry Zito, Mark Mulder, and Tim Hudson). Instead, in 2012, Beane not only had to piece together a lineup full of mostly unknown veterans and rookies, but he also had to construct a pitching staff of underrated parts as well.

Beane’s ability to assemble talent as well as manager Bob Melvin’s coaching staff’s ability to maximize it, through the implementation of platoons and pitching strategy, led to the 2012 Oakland A’s far exceeding anyone’s expectations and the revival of the belief in Moneyball in its original city.