Back before I started getting into girls (if they ever got into me is a different discussion), I fondly remember playing Earl Weaver Baseball on the family PC. The gameplay was awful but it was cool to cobble teams together from players of all eras. I looked through my Dad’s Baseball Encyclopedia and printed out a list of player seasons for a hypothetical player draft on our dot matrix printer. Shockingly, this draft never happened. Where did I think I was going to find enough circle jerks to draft a simulated league of all-star players?
Fast forward a couple decades and, quite fittingly, things came full circle.
My esteemed blurbers at Rotoworld reached out to me at the height of my pandemic ennui and asked if I wanted to join a 20-team league where you drafted 25-man rosters based on player years from 1980-2019 and then simulated a season.
I was mildly intrigued until I realized the catch. We were going to simulate the season and manage our teams BUT the real winner would be judged by team 5×5 stats like in Roto. This is slightly bonkers but I love this twist. One reason I greatly prefer Roto to Points is that I like the challenge of balancing stats that negatively correlate like power, speed, and average. This format ups that challenge by forcing one to balance the stats needed to win simulation games – namely OBP + SLG + defense on the hitting side and pitcher opponent batting average (OAV), BB/9, and HR/9 – with the 5×5 stats AND consider other variables for maximizing a 25 man roster like lefty/right platoons and bullpen usage.
D.J. Short of Rotoworld (who is a damn fine player that I’ve battled with in Tout Wars Mixed and Yahoo! Friends & Family) put together a comprehensive draft review including the full draft result and a paragraph from me on my strategy. So rather than a full draft recap, I will just go over a couple of interesting observations.
First off, here was my roster:
|Round.Pick||Player (Season) – Pos|
|1.5||Pedro Martinez (2000) – SP|
|2.36||Mike Scott (1986) – SP|
|3.45||Jose Bautista (2011) – OF/3B|
|4.76||Ryne Sandberg (1985) – 2B|
|5.85||Paul Goldschmidt (2015) – 1B|
|6.116||Paul O’Neill (1994) – OF|
|7.125||Fernando Valenzuela (1981) – SP|
|8.156||Brady Anderson (1996) – OF|
|9.165||Troy Tulowitzki (2010) – SS|
|10.196||Bernie Williams (1998) – OF|
|11.205||Carlos Beltran (2003) – OF|
|12.236||Mariano Rivera (1996) – RP|
|13.245||Bill Gullickson (1981) – SP|
|14.276||Keith Foulke (1999) – RP|
|15.285||Duane Ward (1991) – RP|
|16.316||Jason Bay (2005) – OF|
|17.325||Dennis Martinez (1992) – SP|
|18.356||Jean Segura (2016) – SS/2B|
|19.365||Mike Stanley (1994) – C|
|20.396||Mickey Tettleton (1991) – C|
|21.405||Ron Davis (1981) – RP|
|22.436||Kevin Seitzer (1987) – 1B/3B|
|23.445||Pat Listach (1992) – SS|
|24.476||Jose DeLeon (1989) – SP|
|25.485||Gabe White (2000) – RP|
- A big challenge as part of draft prep was to ‘normalize’ the raw stats to each season’s hitting/pitching environment. 40+ HRs in the 1980’s was rare (aside from 1987) and would be valued more than 40+ HRs in the roided-up late 90’s. I had a head start on many of the drafters because of my Historical Player Rater project where I had come up with 5×5 values for all player seasons since 1903.
- While I had never played WhatIfSports (or any other simulator), I was well-versed in how they simulate as the hitter vs pitcher matchup simulator uses the same calculations (Log5) as the Razzball daily projections. That said, it was very helpful to research the FAQs and forums to understand some of the assumptions made for hitters and pitchers. The central assumption they make is that all of a player’s stats are ‘skill’ – e.g., if a pitcher had a BABIP of .220, that will be their BABIP in the simulator. It makes sense that managers expect players to produce similar stats to reality but it is also kind of crazy that 1985 John Tudor could conceivably put up similar sub-2.00 ERA/sub 1.00 WHIP to 1985 Dwight Gooden despite 100 less K’s. The simulator also ignores actual home park factors so Larry Walker’s insane Coors stats are treated the same as 80’s Astros playing in the Astrodome. (All the sim games are in the Nationals’ home park which is completely neutral for HRs). This made the analysis simpler albeit less realistic – e.g., just use actual WHIP vs FIP, naturally gravitate towards hitters in hitter parks and pitchers in pitcher parks.
- Since we are judged solely by team stats, there are some unique correlations when it comes to stats including Wins + Saves and Wins + Runs/RBI (i.e., a high scoring team with poor ERA/WHIP could still get a lot of Wins). I ended up going with a pitcher-heavy strategy as I think K/ERA/WHIP correlate better than R/HR/RBI/SB/AVG – i.e., it was easier to build a great pitching / good offense team vs good pitching / great offense team.
- You always want to anticipate and exploit the biases of a draft room. This was such a novel setup that it is hard to figure out the biases. My assumption going in was that the room would skew hitter vs pitcher and more recent years vs previous years. I also hoped that drafters would focus too much on power/speed and I could build a monster OBP + fielding team to crush on Runs/RBI/AVG (which would also help my Wins/Saves).
- These assumptions held true to varying degrees except for OBP which was well-accounted for by the drafters.
- Despite 7 SPs going in the top 20, the hit/pitch split was around 65/35 where I thought that, since pitchers cannot get hurt, the split should be more like 55/45. I believe I invested the most in pitching with about a 52/48 split spent predominantly on SP1-SP3.
- Below are the splits by decade. I ended up with 17 players from 1980-1999 and 8 from 2000-2019 which was definitely against the tide.
Decade H % SP % RP % 1980’s 43 16% 23 20% 17 15% 1990’s 70 26% 19 17% 16 14% 2000’s 79 29% 22 19% 26 22% 2010’s 78 29% 50 44% 57 49% Total 270 114 116
- I did end up with a strong OBP/fielding team but I had to sacrifice power to get there. I only had two hitters with over 34 HRs (Brady Anderson, Jose Bautista) which is not ideal. All of my starters are 20+ HRs so I am hoping to get to league average through depth. I was able to build up some speed though on the bench with Jean Segura and Pat Listach which I could hopefully unleash against weaker armed catchers.
- One drafting quirk I did not anticipate was the hitter/pitcher split. I had assumed most would go 14 hitters / 6-7 SP / 4-5 RP but the room averaged 5.7 SPs and 5.8 RPs (11.5 pitchers). Now that I have seen the simulation in action, I think this was a case the room was smarter than me. While I am happy with my 6 SP / 5 RP staff, I would gladly trade my SP6 (Jose DeLeon) for another reliever. I also likely overrated the advantage of drafting high IP SPs (altough maybe I will benefit from other teams’ pitchers fatiguing more).
- I grew up a Yankees fan but gave up my fandom in the mid 2000’s when the salary disparity got too gross. While it was not a particular plan, I did go to town on mid 90’s Yankees with Paul O’Neill, Bernie, Mike Stanley, and Mariano Rivera.
Overall, I feel good about the draft. It was by far the most fun I ever had chatting with others in a draft room (I rarely say more than ‘could we move a little quicker’ in draft rooms). Here’s hoping honorary coach Gene ‘Stick’ Michael and hitting/pitching coaches Oscar Gamble and Rudy May could help me bring home the pennant!