Moneyball Recruiting - Quia



Moneyball Recruiting and Sabermetrics

Excerpted from Wikipedia

If you’re unfamiliar with Moneyball, the term comes from Moneyball: The Art of Winning an Unfair Game, a book by Michael Lewis about the Oakland Athletics baseball team and its general manager Billy Beane. Its focus is the team’s modernized, analytical, sabermetric approach to assembling a competitive baseball team, despite Oakland’s disadvantaged revenue situation. Simply put, the Oakland A’s didn’t have the money to buy top players, so they had to find another way to be competitive.

The central premise of Moneyball is that the collected wisdom of baseball insiders (including players, managers, coaches, scouts, and the front office) over the past century with regard to player selection is subjective and often flawed.

Through the use of rigorous statistical analysis of baseball player performance and game records and Sabermetrics, the Oakland Athletics picked players based on qualities that flew in the face of conventional baseball wisdom and the beliefs of many baseball scouts and executives. In 2002, with approximately $41 million in salary, the Oakland A’s were competitive with larger market teams such as the New York Yankees, who spent over $125 million in payroll that same season.

When Sabermetrics was introduced into baseball, it was immediately rejected by many simply because it was new, different, leveraged statistics over intuition and experience, and frequently questioned conventional wisdom with regard to traditional measures of baseball skill evaluation. For instance, Sabermetricians doubt that batting average is as useful as conventional wisdom says it is because team batting average provides a relatively poor fit for team runs scored.

While baseball traditionalists scoff at the sabermetric revolution and have disparaged Moneyball for emphasizing concepts of sabermetrics over more traditional methods of player evaluation, the impact of Moneyball upon major league front offices is undeniable.

For example, teams such as the New York Mets, New York Yankees, San Diego Padres, St. Louis Cardinals, Boston Red Sox, Washington Nationals, Arizona Diamondbacks, Cleveland Indians, and the Toronto Blue Jays have hired full-time Sabermetric analysts.

For more information on Sabermetrics, go to

Moneyball and Sabermetrics Project

For this project, you are going to compare 5 batters and 3 pitchers from one major league team to 5 batters and 3 pitchers from another major league team. If you need help choosing the players, let me know and I can help. I would prefer that you choose one small market team (from a small city) to a large market team (from a big city). You can research which cities are small market and which are large market teams.

Team 1:__________________ Team 2: ____________________

Batter 1: _________________ Batter 1:____________________

Batter 2: _________________ Batter 2:____________________

Batter 3: _________________ Batter 3:____________________

Batter 4: _________________ Batter 4:____________________

Batter 5: _________________ Batter 5:____________________

Pitcher 1: _________________ Pitcher 1:____________________

Pitcher 2: _________________ Pitcher 2:____________________

Pitcher 3: _________________ Pitcher 3:____________________

Rigorous statistical analysis had demonstrated that on-base percentage and slugging percentage are better indicators of offensive success, and the A's became convinced that these qualities were cheaper to obtain on the open market than more historically valued qualities such as speed and contact. These observations often flew in the face of conventional baseball wisdom and the beliefs of many baseball scouts and executives.

By re-evaluating the strategies that produce wins on the field, the 2002 Athletics, with approximately $41 million in salary, were competitive with larger market teams such as the New York Yankees, who spent over $125 million in payroll that same season. Because of the team's smaller revenues, Oakland is forced to find players undervalued by the market, and their system for finding value in undervalued players has proven itself thus far.

For each of the Batters above, you need to compute the following stats:

OBP--On base percentage

SLG---Slugging percentage

OBS---On base + Slugging

XRB—Extrapolated Run Basic

SEC-A--Secondary average

To compute these you will need to find the following statistics (or compute them)

• H = Hits

• BB = Bases on Balls (Walks)

• HBP = times Hit By a Pitch

• AB = At bats

• SF = Sacrifice Flies

• 1B—Single: hits on which the batter reaches first base safely without the contribution of a fielding error.

• 2B—Double: hits on which the batter reaches second base safely without the contribution of a fielding error.

• 3B—Triple: hits on which the batter reaches third base safely without the contribution of a fielding error.

• AB—At bat: Plate appearances, not including bases on balls, being hit by pitch, sacrifices, interference, or obstruction.

• TBB-Total base on balls

• SB- Stolen Bases

• HBP-Hit by pitch

• CS- Caught Stealing

Research ten different batters (five from each team) and find the above statistics for them. On the tables provided on excel, fill in the cells for each of the above categories in order to compute the 5 sabermetric categories for batters. Find pitching statistics—this will be a separate table. Fill in your pitching stats for your 6 pitchers. (Note: sheet 2 of the spreadsheet contains the pitching data—fill in HR, BB, HBP, K, IP).

Calculations and Formulas:

Below are the formulas for computing each of the sabermetric calculations I want you to compare.

On Base Percentage: The percentage a batter gets on base.

[pic]

Slugging:

Slugging percentage (abbreviated SLG) is a popular measure of the power of a hitter. It is calculated as total bases divided by at bats:

[pic]

On base plus slugging:

The ability of a player to both get on base and to hit for power, two important hitting skills, are represented. An OPS of .900 or higher in Major League Baseball puts the player in the upper echelon of hitters. Typically, the league leader in OPS will score near, and sometimes above, the 1.000 mark.

[pic]

Extrapolated Runs Basic

A baseball statistic invented by sabermetrician Jim Furtado to estimate the number of runs a hitter contributes to his team. XRB measures essentially the same thing as Bill James' Runs Created, but it is a linear weights formula that assigns a run value to each event, rather than a multiplicative formula like James' creation

XRB – Extrapolated Runs Basic = (.50 × 1B) + (.72 × 2B) + (1.04 × 3B) + (1.44 × HR) + (.34 × (TBB)) + (.18 × SB) + (−.32 × CS) + (−.096 × (AB − H))

Secondary average, or SecA

[pic]

Secondary average, or SecA, is a baseball statistic - more precisely, a sabermetric measurement of hitting performance. It is a complement to batting average, which is a simple ratio of base hits to at bats. Secondary average is a ratio of bases gained from other sources (extra base hits, walks and net bases gained through stolen bases) to at bats. Secondary averages have a higher variance than batting averages.

In modern Major League Baseball, a secondary average higher than about .500 is considered outstanding, and one below .200 is considered very poor. The league average SecA is typically similar to the league average batting average, in the range of .250-280.

Questions

1. Now find the salaries of each of the 10 batters and 6 pitchers you studied. Fill them in on another column in your chart.

2. According the moneyball stats, compare the individual players and identify if they are a good buy.

3. Compare the small market batters salaries and stats to the large market. Are they different? How so?

4. Do the small market teams tend to make better buys? Explain.

Pitchers

Research six different batters (three from each team) and find the pitching statistics for them (HR, BB, HBP, K, IP). Copy and paste their 2011 statistics here:

Defense independent pitching statistics

Jump to: navigation, search

In baseball, defense-independent pitching statistics (DIPS) measure a pitcher's effectiveness based only on plays that do not involve fielders: home runs allowed, strikeouts, hit batters, walks, and, more recently, fly ball percentage, ground ball percentage, and (to much a lesser extent) line drive percentage. Those plays are under only the pitcher's control in the sense that fielders (not including the catcher) have no effect on their outcome.

Several sabermetric methods use only these "defense-independent" pitching statistics to evaluate a pitcher's ability. The logic behind using only these statistics is that once a ball is put in play, most often the pitcher has no effect on the resultant fielding of the ball. But defense-dependent statistics, such as the rate of hits allowed on balls put into play, are sometimes affected by the quality and/or arrangement of the defense behind the pitcher. For example, an outfielder may make an exceptionally strong diving catch to prevent a hit, or a base runner may beat a play to a base on a ball thrown from a fielder with sub par arm strength. Defense-independent statistics such as walks and strikeouts are determined almost solely by the pitcher's ability level.

[pic]

Team 1: _________________ Team 2:____________________

Pitcher 1: _________________ Pitcher 1:____________________

Pitcher 2: _________________ Pitcher 2:____________________

Pitcher 3: _________________ Pitcher 3:____________________

................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download