For example, did rosters and day-to-day lineups match real-life? Was the actual schedule used or did the Dodgers get stuck playing Boston and the Yankees in inter-league play while the Giants played Seattle and Texas? Did injuries match real-life? If you're looking for answers as to why the W-L records were so far off, I'd look for answers to those questions after first examining the each club's Pythagorean W-L expectation. There could be many reasons for this besides just the pure chance of a replay. The big changes in the division would seem to be the over-performing Diamondbacks and Padres (and the Giants). So while they did under-perform, they weren't that far off the mark. Your replay shows them, yes, in 4th, but at 84-78 (.519). Throw in a 2-3 win margin of error for each club and it's not unusual to see replays where the Dodgers finish 20 out, even with both teams' rosters generally having good statistical comparisons.Īs for your OOTP replay, the 2016 Dodgers went 91-71 in real-life. Their Pythagorean W-L expectation identical 91-71 (.562). But based on how many runs the two teams scored and allowed, they would have been projected to finish 15 behind. In real-life, the Dodgers finished 10 games behind Cincinnati. So it is not surprising to see this Reds club win 108-112 games in a DMB replay. That projects to an expected 105-57 season. The Big Red Machine scored 857 in real-life and gave up 633. On the flip side, the Cincinnati Reds usually do better in DMB than in real-life (102-60. Their Pythagorean W-L expectation was 90-72 (.556). That season the Dodgers scored 608 and gave up 543. I think what would be a more important comparison is not the W-L records, but their Pythagorean W-L expectations in both real-life and in your replay.įor example, in my DMB replays the 1976 Dodgers generally do worse than their 92-70 actual record (.568).Ī closer look would show why. I had a discussion about W-L records years ago on another forum. Over the course of 13 simulations, the results came closer to actual standings.īottom line - DMB will give you much better accurate individual/team results, OOTP will immerse you in the simulated GM aspect of the game. OOTP got 2 of 6 divisions correct but were way off on others - but again, it is just a simulated season. I put together a side by side comparison of OOTP simulated season vs Actual Standings for 2016. My frustration was playing the 2016 season, and the Padres ending up winning the division and the Dodgers coming in 4th.Ĭorrection - I double checked and the Giants actually won the division, and the Padres came in 2nd. ![]() If you play entire seasons in a monte carlo simulation, the team results/standings come closer to actual results. I play OOTP strictly as season replays playing the Dodgers. The biggest problem is that though they try to simulate real life stats, it is at the league level, and does NOT simulate individual player stats closely. They are up to date on all sabermetric stats - which is something I wish DMB would update. I play games as the manager for all 16 teams that I use.įor OOTP, it has a modern interface, and tons of detail for GM and financial management. I set up a league each season using the top 5 teams from each league, and then create an all star team for each division. DMB is still my favorite game, but I also enjoy the GM detail and reporting capabilities of OOTP.įor me, DMB provides a very realistic simulation of how players performed each season. I started also playing OOTP 16 last year. I've been playing DMB since it was Pursue the Pennant as a board game in the mid 90s.
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