In part 1 I introduced this concept and in part 2 I determined my Opening Day team. But to answer the question regarding how such a team would do gave me a lot of trouble, and took a different turn than I expected.

Initially I believed I could use a simple WAR calculator to see just how well my players would do and use that guide to determine the team’s fate. Yet to figure those factors out I would need to calculate a player’s OPS and slugging percentage as well as a pitcher’s ERA. So my first order of business was determining about how many plate appearances each player would get; thus, I made a matrix covering the nine starting positions and also determined how many starts and relief appearances each pitcher would make. From there I calculated the rest of the statistics based on the players’ real-life numbers and some overall averages.

Using my team’s starting lineup and their WAR, this is the comparison to the Orioles 2018 lineup.

2019 WAR | SotW team | Pos. | Baltimore | 2018 WAR |

1.5 | A. Wynns | C | C. Joseph | 0.3 |

2.0 | T. Mancini | 1B | C. Davis | -2.8 |

2.3 | J. Schoop | 2B | J. Schoop | 1.3* |

1.5 | P. Florimon | SS | M. Machado | 2.9* |

7.5 | M. Machado | 3B | R. Nunez | 1.2* |

0.4 | Delmonico | LF | T. Mancini | -0.1 |

1.5 | C. Mullins | CF | A. Jones | 0.2 |

-0.3 | L.J. Hoes | RF | J. Rickard | 0.4 |

0.9 | C. Walker | DH | M. Trumbo | 0.3 |

2.9 | E. Rodriguez | SP | D. Bundy | 0.1 |

2.7 | Z. Davies | SP | A. Cashner | 0.6 |

3.1 | D. Bundy | SP | A. Cobb | 1.1 |

0.9 | S. Brault | SP | K. Gausman | 2.2* |

1.0 | P. Bridwell | SP | D. Hess | 0.7 |

0.8 | Z. Britton | CL | B. Brach | 0* |

1.1 | J. Hader | RP | M. Castro | 1.3 |

1.3 | M. Givens | RP | M. Wright | -0.1 |

0.8 | Hernandez | RP | M. Givens | 1 |

-0.8 | E. Gamboa | RP | T. Scott | -0.1 |

31.1 | Total WAR | Pos. | Total WAR | 10.5 |

But the one thing about WAR is that it’s a relatively inexact science. Still, using the simple WAR calculators for pitchers and batters, I came up with a team WAR of 32.3 for my mythical 40-man roster. That turns out to be 21 wins better than the 2018 Orioles (meaning 68 wins) and nearly 25 fewer wins than the Red Sox, which would compute to an above break-even season with 83 wins. To me, that was a little too much of a range.

So I tried a different way. Since I had figured out most of the main batting stats in order to define OPS and slugging percentage for the hitters, I decided to treat the pitchers the same way and figure out the batting stats against them. Once I had those numbers, I pored over about two decades’ worth of team batting stats to determine the closest parallels to runs scored based on average, on-base percentage, slugging percentage, and OPS, numbers which I averaged together to determine projected totals of runs scored and runs allowed, which then allowed me to figure out a Pythagorean win-loss record that’s relatively accurate – most teams finish within a few games of their Pythagorean record.

**On that basis, my team would finish with a surprisingly good record of 72-90.** I say surprisingly because it would finish near the bottom of both the batting and pitching rankings; then again, these align well with the rankings of the 2018 American League teams as five teams finished with fewer than 72 wins and this team generally laid in the bottom third statistically. Presumably it would be a rather strong bullpen that carries my team if they get an early lead.

One other thing all this calculation allowed me to do was change the roster somewhat. (This was reflected in the posts as I did the statistics before the second post where I selected the team.) In one instance, Christian Walker was not a full-time DH but was ticketed for AAA – however, in figuring out his season he had a bat that was too good to send down in comparison to my outfielders – so he stayed. And since his real-life MLB experience has mostly come as a pinch-hitter he’s a natural DH. Other players got more starts than originally envisioned because they were the best player I could put out there despite not being “established.” I also took the propensity for injuries into account so several of my players missed time on the “disabled list” and others were “called up” to replace them. For example, Pedro Florimon has been an injury magnet the last few seasons so in my mythical campaign he missed some time, enabling Manny Machado to slide over to short and placing utility players at third. Players who are well short of a full season are usually considered to be injured for a portion of it.

So I have not only answered my question, but I’ve also created a projected set of statistics (set in pretty much the same fashion as *Baseball Reference* lays out statistics) for each player based on a weighted formula of previous seasons and levels – thus, a guy who played at AAA a lot has his numbers adjusted a few ticks lower where appropriate. Raw rookies took a bit of a pounding from this, but if I continue to update these numbers they will settle in closer to their eventual MLB norms. It also gives me the fun of seeing how numbers will compare to real life as 2019 progresses.

(One note: for players who have retired I simply used their previous 4 active seasons, disregarding the layoff factor. It was as if they were still playing.)

This was a very fun and challenging exercise – but since I still have the numbers I could do it again for next spring as new players join the SotWHoF. It will actually be easier since I gave the now-retired players a courtesy cup of coffee (maybe a latte in a couple cases) in this mythical season but won’t feel the need to in 2020, unless I get in a positional pinch. (For example: if Michael Ohlman doesn’t find a team this year I still need him as a third catcher unless a guy like onetime SotW Wynston Sawyer gets the call.)

But consider this as you watch the 2019 season unfold and see how bad my projections are: at least free agency won’t break up this team! Thanks for playing along.