Recently, Steve Slowinski of
Fangraphs authored an article where he listed the 2011 All-Flub Teams. Steve included both an NL and AL team of players who are the kings of underachievers from both leagues. In the article Steve posed the question of which of these two teams would win a head to head matchup. I volunteered to run the two teams through my baseball simulator to find out. I ran two simulations, one with each league's flops as the home team. I selected Sean O'Sullivan as the American League's top flop pitcher and Armando Galarraga as the National League's starting pitcher. Each matchup was simulated 100K times. I also used neutral park factors for all of the simulated games. Below are the results as well as some of the stats.
Away
| Home
| Starting Pitchers
| Favorite
| Win Probability
| Total Runs
|
American
| National
| S.O'Sullivan vs A.Galarraga
| National
| 51.567%
| 9.69
|
National
| American
| A.Galarraga vs S.O'Sullivan
| American
| 56.979%
| 9.71
|
Results: The simulator thinks that the American League team is slightly better. If you average the win probability of the away/home series the American League wins 52.706% of the time, with right around 9.7 runs scoring on average. Below are some of the box score stats taken from the matchup where the National League was the home team.
Pitchers Boxscores
Name | IP | SO | BB | Hits | HR | PC | FIP |
Sean O'Sullivan | 5.697 | 3.025 | 1.982 | 6.841 | 0.748 | 91.555 | 4.888 |
Jon Rauch | 0.445 | 0.343 | 0.101 | 0.465 | 0.051 | 6.82 | 3.819 |
Mike Gonzalez | 0.413 | 0.364 | 0.168 | 0.432 | 0.063 | 6.791 | 4.641 |
Daniel Schlereth | 0.67 | 0.573 | 0.352 | 0.721 | 0.108 | 11.458 | 5.158 |
Jason Berken | 0.419 | 0.317 | 0.144 | 0.471 | 0.071 | 6.775 | 4.912 |
Adam Russell | 0.699 | 0.365 | 0.324 | 0.805 | 0.058 | 11.53 | 4.632 |
Joe Nathan | 0.343 | 0.298 | 0.119 | 0.365 | 0.052 | 5.551 | 4.484 |
Andy Sonnanstine | 0.018 | 0.009 | 0.006 | 0.022 | 0.003 | 0.286 | 5.647 |
| | | | | | | |
Armando Galarraga | 5.808 | 3.164 | 2.146 | 7.073 | 0.621 | 94.435 | 4.609 |
Ryan Franklin | 0.409 | 0.3 | 0.085 | 0.467 | 0.061 | 6.337 | 4.298 |
Michael Dunn | 0.429 | 0.445 | 0.193 | 0.414 | 0.044 | 7.149 | 3.8 |
Pat Neshek | 0.712 | 0.564 | 0.367 | 0.784 | 0.087 | 12.131 | 4.745 |
Sean Burnett | 0.483 | 0.351 | 0.168 | 0.535 | 0.045 | 7.771 | 3.996 |
Miguel Batista | 0.97 | 0.637 | 0.46 | 1.122 | 0.086 | 16.277 | 4.461 |
John Grabow | 0.354 | 0.228 | 0.158 | 0.42 | 0.044 | 5.916 | 4.856 |
Aneury Rodriguez | 0.007 | 0.005 | 0.002 | 0.008 | 0.001 | 0.114 | 4.366 |
Hitters Boxscore
Name | AB | Hits | H1 | H2 | H3 | HR | RBI | BB | SO | wOBA | BABIP |
Juan Pierre | 4.57 | 1.406 | 1.138 | 0.17 | 0.039 | 0.06 | 0.399 | 0.42 | 0.371 | 0.343 | 0.325 |
Chone Figgins | 4.389 | 1.368 | 0.959 | 0.324 | 0.02 | 0.064 | 0.466 | 0.406 | 0.607 | 0.357 | 0.351 |
Alex Rios | 4.457 | 1.374 | 0.918 | 0.323 | 0.019 | 0.114 | 0.59 | 0.279 | 0.54 | 0.354 | 0.331 |
Justin Morneau | 4.2 | 1.27 | 0.773 | 0.327 | 0.011 | 0.16 | 0.673 | 0.481 | 0.588 | 0.379 | 0.322 |
Adam Dunn | 3.923 | 1.013 | 0.497 | 0.29 | 0.004 | 0.221 | 0.694 | 0.651 | 1.178 | 0.375 | 0.314 |
David Murphy | 3.962 | 1.218 | 0.813 | 0.237 | 0.02 | 0.148 | 0.601 | 0.491 | 0.589 | 0.382 | 0.332 |
Orlando Cabrera | 4.08 | 1.168 | 0.778 | 0.297 | 0.013 | 0.081 | 0.503 | 0.221 | 0.47 | 0.327 | 0.308 |
Cliff Pennington | 3.866 | 1.092 | 0.754 | 0.239 | 0.036 | 0.063 | 0.449 | 0.325 | 0.64 | 0.331 | 0.325 |
Jeff Mathis | 3.785 | 0.915 | 0.594 | 0.238 | 0.006 | 0.077 | 0.429 | 0.305 | 0.711 | 0.295 | 0.28 |
| | | | | | | | | | | |
Mike Cameron | 4.31 | 1.245 | 0.694 | 0.375 | 0.043 | 0.133 | 0.497 | 0.381 | 0.615 | 0.36 | 0.312 |
Ryan Spilborghs | 4.129 | 1.226 | 0.76 | 0.293 | 0.017 | 0.157 | 0.538 | 0.442 | 0.671 | 0.371 | 0.324 |
Aubrey Huff | 4.032 | 1.151 | 0.719 | 0.246 | 0.029 | 0.158 | 0.56 | 0.509 | 0.549 | 0.368 | 0.299 |
Chris Johnson | 4.157 | 1.231 | 0.766 | 0.317 | 0.031 | 0.118 | 0.589 | 0.23 | 0.733 | 0.348 | 0.337 |
Raul Ibanez | 3.88 | 1.132 | 0.672 | 0.272 | 0.028 | 0.16 | 0.614 | 0.447 | 0.623 | 0.374 | 0.314 |
Casey McGehee | 3.832 | 1.113 | 0.69 | 0.28 | 0.004 | 0.139 | 0.561 | 0.331 | 0.548 | 0.356 | 0.31 |
Bill Hall | 3.761 | 1.037 | 0.59 | 0.298 | 0.034 | 0.115 | 0.498 | 0.296 | 0.697 | 0.343 | 0.313 |
Yuniesky Betancourt | 3.761 | 1.067 | 0.699 | 0.242 | 0.034 | 0.093 | 0.464 | 0.187 | 0.364 | 0.329 | 0.295 |
Dioner Navarro | 3.473 | 0.918 | 0.592 | 0.227 | 0.019 | 0.081 | 0.398 | 0.373 | 0.493 | 0.33 | 0.289 |
And here is a bar chart showing how the run totals are distributed. The x-axis the total runs scored in the game and the y-axis is how many occurrences this happened. As you can tell from the chart a run total of 7 and 9 runs is the most common occurrence.