Use of Data Analytics in Sports. Movie :- Moneyball depicts the usage of Analytics

2. INTRO
The Oakland Athletics are a Major League Baseball team based in Oakland,
California.
The Athletics are a member of the Western Division of Major League Baseball's
American League. From 1968 to the present, the Athletics have played in the
Oakland Coliseum since moving to Oakland. Overall, the A's have won nine World
Series championships, the third-best total in Major League Baseball (trailing only
the New York Yankees and the St. Louis Cardinals).
In 2002, the A's won 20 games in a row, which broke an AL record, as shown in the
film Moneyball. The movie, and the book which the movie was derived,
showcased how the A's were able to compete and thrive despite their financial
limitations.

3. BUSINESS PROBLEM
The Business Problem: the New York Yankees were the most acclaimed team in Major League
Baseball. Small market teams like Oakland Athletics (Oakland A’s) had to change the way they
did business. The A’s were not a wealthy team, in fact were ranked 12th (out of 14th) in
payroll.
● A core strategy question for the A’s (and in sports) is: How to compete with rich teams with
constraints like salary caps and small market economics? How to spot and acquire low-cost
undervalued talent that is a “force multipler” and not a “money blackhole”?
● The Solution: In 1999 Billy Beane (manager for the Oakland Athletics) found a novel use of data
mining. Beane hired a statistics grad to analyze baseball statistics (pitcher’s records, RBI,
batting average in MLB and minors) advocated by baseball guru Bill James. Beane was able
to hire excellent players undervalued by the market. A year after Beane took over, the A’s
ranked 2nd!
● Beane understood that traditional statistics used to judge players and prospects was flawed and
there were better predictors of success.

4. OAKLAND APPROACH/STRATEGIES
● The principle behind Beane’s approach is that he needs to put together a team that
would regularly score more runs than its opponent. He would do this by reverse
engineering how a team scores runs (gets on base, advances bases, etc.) and then
finding players (within his budget) who will generate more run production than they
allow. This could be achieved by scoring more or limiting their opponent’s run
production.
● How did they do it? While the Yankees paid its star players tens of millions, the A’s
managed to compete with a low payroll. When signing players, they didn’t just look at
basic productivity values such as RBIs, home runs, and earned-run averages. Instead,
they analyzed hundreds of variables from every player and every game, attempting to
predict future performance and production. Past performance as a predictor of the
future.
● 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.

5. STATS - HITTING
AVG – Average Number of Hits defined by defined by hits divided by at bats (H/AB)
OBP – The number of times each batter reaches base by hit, walk or hit by pitch, divided by plate appearances
including at bats, walks, hit by pitch and sacrifices (H+BB+HBP)/(AB+BB+HBP+SF)
SLG – Slugging Percentage. The measure of the power of hitter, total bases divided by at bats (TB/AB)
OPS – On-Base Percentage plus slugging (OBP + SLG)
RK
Player Year Team Pos G AB R H 2B 3B HR RBI BB SO SB CS AVG▼ OBP SLG OPS
1 * Giambi, J 2001 OAK 1B 154 520 109 178 47 2 38 120 129 83 2 0 0.342 0.477 0.66 1.137
2 * Giambi, J 2000 OAK 1B 152 510 108 170 29 1 43 137 137 96 2 0 0.333 0.476 0.647 1.123
3 * Giambi, J 1999 OAK 1B 158 575 115 181 36 1 33 123 105 106 1 1 0.315 0.422 0.553 0.975
4 * Kotsay, M 2004 OAK CF 148 606 78 190 37 3 15 63 55 70 8 5 0.314 0.37 0.459 0.829
5 * Tejada, M 2002 OAK SS 162 662 108 204 30 0 34 131 38 84 7 2 0.308 0.354 0.508 0.861
6 * Donaldson, J 2013 OAK 3B 158 579 89 174 37 3 24 93 76 110 5 2 0.301 0.384 0.499 0.883
7 * Giambi, J 1998 OAK 1B 153 562 92 166 28 0 27 110 81 102 2 2 0.295 0.384 0.489 0.873
8 * Sweeney, R 2009 OAK RF 134 484 68 142 31 3 6 53 40 67 6 5 0.293 0.348 0.407 0.755
9 * Giambi, J 1997 OAK 1B 142 519 66 152 41 2 20 81 55 89 0 1 0.293 0.362 0.495 0.857
10 * Cespedes, Y 2012 OAK LF 129 487 70 142 25 5 23 82 43 102 16 4 0.292 0.356 0.505 0.861
11 * Giambi, J 1996 OAK 1B 140 536 84 156 40 1 20 79 51 95 0 1 0.291 0.355 0.481 0.836
12 * Lowrie, J 2013 OAK SS 154 603 80 175 45 2 15 75 50 91 1 0 0.29 0.344 0.446 0.791
13 * Kennedy, A 2009 OAK 2B 129 529 65 153 29 1 11 63 45 86 20 6 0.289 0.348 0.41 0.758
14 * Chavez, E 2001 OAK 3B 151 552 91 159 43 0 32 114 41 99 8 2 0.288 0.338 0.54 0.878
15 * Davis, R 2010 OAK CF 143 525 66 149 28 3 5 52 26 78 50 11 0.284 0.32 0.377 0.697
16 * Chavez, E 2003 OAK 3B 156 588 94 166 39 5 29 101 62 89 8 3 0.282 0.35 0.514 0.864
17 * Kotsay, M 2005 OAK CF 139 582 75 163 35 1 15 82 40 51 5 5 0.28 0.325 0.421 0.746
18 * Suzuki, K 2008 OAK C 148 530 54 148 25 1 7 42 44 69 2 3 0.279 0.346 0.37 0.716
19 * Tejada, M 2003 OAK SS 162 636 98 177 42 0 27 106 53 65 10 0 0.278 0.336 0.472 0.807

6. STATS - PITCHING
RK Player Year Team W L ERA▲ G GS SV SVO IP H R ER HR BB SO AVG WHIP
1 * Colon, B 2013 OAK 18 6 2.65 30 30 0 0 190.1 193 60 56 14 29 117 0.264 1.17
2 * Hudson, T 2003 OAK 16 7 2.7 34 34 0 0 240 197 84 72 15 61 162 0.223 1.08
3 * Zito, B 2002 OAK 23 5 2.75 35 35 0 0 229.1 182 79 70 24 78 182 0.218 1.13
4 * Cahill, T 2010 OAK 18 8 2.97 30 30 0 0 196.2 155 73 65 19 63 118 0.22 1.11
5 * Hudson, T 2002 OAK 15 9 2.98 34 34 0 0 238.1 237 87 79 19 62 152 0.263 1.25
6 * Haren, D 2007 OAK 15 9 3.07 34 34 0 0 222.2 214 91 76 24 55 192 0.247 1.21
7 * Gonzalez, G 2011 OAK 16 12 3.12 32 32 0 0 202 175 81 70 17 91 197 0.23 1.32
8 * Mulder, M 2003 OAK 15 9 3.13 26 26 0 0 186.2 180 66 65 15 40 128 0.259 1.18
9 * Gonzalez, G 2010 OAK 15 9 3.23 33 33 0 0 200.2 171 75 72 15 92 171 0.229 1.31
10 * Zito, B 2003 OAK 14 12 3.3 35 35 0 0 231.2 186 98 85 19 88 146 0.219 1.18
11 * McCarthy, B 2011 OAK 9 9 3.32 25 25 0 0 170.2 168 73 63 11 25 123 0.258 1.13
12 * Hudson, T 2001 OAK 18 9 3.37 35 35 0 0 235 216 100 88 20 71 181 0.245 1.22
13 * Mulder, M 2001 OAK 21 8 3.45 34 34 0 0 229.1 214 92 88 16 51 153 0.249 1.16
14 * Mulder, M 2002 OAK 19 7 3.47 30 30 0 0 207.1 182 88 80 21 55 159 0.232 1.14
15 * Parker, J 2012 OAK 13 8 3.47 29 29 0 0 181.1 166 71 70 11 63 140 0.248 1.26
16 * Zito, B 2001 OAK 17 8 3.49 35 35 0 0 214.1 184 92 83 18 80 205 0.23 1.23
17 * Braden, D 2010 OAK 11 14 3.5 30 30 0 0 192.2 180 83 75 17 43 113 0.249 1.16
ERA – The average number of earned runs allowed by a pitcher; total number of earned runs allowed multiplied
by 9 divided by the number of innings pitched. ((ERx9)/IP)
AVG – The total number of hits allowed by the pitcher divided by the total number of opponent at-bats (H/AB)
WHIP – The average number of walks and hits by the pitcher, Hits plus walks allowed divided by innings pitched
((H+W)/IP)

7. STATS - FIELDING
RK Player Team Pos G GS INN TC PO A E DP SB CS SBPCT PB C_WP FPCT▼ RF
1 Crisp, C OAK CF 110 107 919.0 309 307 2 0 1 - - - - - 1.000 2.81
1 Griffin, A OAK P 32 32 200.0 23 8 15 0 0 - - - - - 1.000 0.72
3 Norris, D OAK C 91 71 663.0 595 562 30 3 4 35 12 .745 6 23 .995 6.51
4 Moss, B OAK 1B 111 88 801.2 717 663 47 7 52 - - - - - .990 6.40
5 Sogard, E OAK 2B 113 98 865.0 472 207 258 7 68 - - - - - .985 4.12
6 Reddick, J OAK RF 113 108 966.1 258 244 9 5 3 - - - - - .981 2.24
7 Colon, B OAK P 30 30 190.1 29 6 22 1 1 - - - - - .966 0.93
8 Lowrie, J OAK SS 119 115 1023.1 421 139 266 16 56 - - - - - .962 3.40
9 Donaldson, J OAK 3B 155 155 1373.0 414 143 255 16 22 - - - - - .961 2.57
10 Parker, J OAK P 32 32 197.0 39 14 21 4 0 - - - - - .897 1.09
FPCT – Average of errors per total chances, Put outs plus Assists divided Putouts plus assists plus error
(PO+A)/(PO+A+E)
PO – A putout is credited to a fielder when catches a fly ball or a line drive, whether fair or foul, catches a thrown
ball which puts out a batter or runner, or tags a runner when the runner is off the base to which he legally is
entitled
A – Total Assists
E – Total Errors committed

8. OTHER STATS
Sortable Team
Top Rookies

9. OTHER STATS …
Batter Vs Pitcher
Milestone Tracker

10. CONCLUSION
● Analytics can help you win even if you have limited resources.
● Constantly reinvent.
● It is not a competitive advantage when everyone does it.