By request, I present all of MLB teams results and rankings in drafting-and-developing
In the recent 2024 update to Draft and Develop results, I focused the article on how the Cardinals compare with other top draft-and-develop teams. I showed the results for the top 6 (ie. the top 20%) but didn’t really go into more detail for the remainder of the league.
Several people asked to see this, and this article fills that gap.
First, a reminder of the methods used (my own imperfect home brew). I tally up the career WAR of each player drafted and assigned that WAR to the drafting team. Some players were drafted twice. Both teams get credit for identifying the correct talent. A couple players were drafted thrice – one Ian Kinsler, for example.
I do not, in this iteration of the model, discriminate between WAR accumulated with the original drafting team from WAR accumulated with another team. I did last year and found that those results were random, and I couldn’t assign points or rank on those criteria in any reliable way. For instance, some teams that are very poor at drafting don’t give away much WAR, but it is hard to give them points for not giving away value, if they didn’t have any to give away. On the other hand, the Dodgers and Red Sox lead the league in WAR given away, but it appears that is because they have so much, it won’t fit on their 40-man rosters. Hard to penalize them for being so good at drafting they can’t fit everyone. In fact, the WAR the Red Sox have given away would be a top 10 drafting team all by itself.
After accounting for drafted players, I add in “notable” international free agents (think: Shohei Ohtani). There isn’t an available data set for all IFAs, so I hand built a data set for only the notable IFAs. If the pattern from drafted players follows, the international players that are not notable probably don’t amount to enough WAR to swing the rankings materially.
I add a count of drafted and IFA players to the model, to balance out and correct for biases introduced where unicorn type players (read: Mike Trout) accumulate so much WAR by themselves that it swings the model. Think of it this way. Are the Angels better at drafting than all but 4-5 teams just because they lucked into Trout in 2009 and Ohtani in 2018? Their overall team track records say NO. I found in the data that the count of notable players was more correlated to team success (ie. wins) that was the sum of the WAR those players accumulated.
Last, the model awards points based on draft position. Lower drafting teams gets more points than higher drafting teams. One can look at the distribution of total WAR by draft pick position and realize that top 5 picks tend to yield more total WAR than other spots lower in the draft. In a material way. So, the lower drafting team gets goosed a little bit if they are better drafters.
Below is how everyone ranks for all drafting 2000-2024. Realistically, very few players from the last 4 drafts have contributed much in the way of WAR, so it is really an evaluation of drafts from 2000-2019.
Some things I find worth noting. You can look and see what you see.
- Note that the Red Sox drafted the most accumulated WAR for the period at 749 WAR, or roughly 37.5 per year. I have a filterable spreadsheet that will show each of those players. As with any team, there is a bit of “I had no idea the Red Sox drafted him….”.
- The Dodgers rank higher due to having a better track record in the IFA arena and spreading the WAR out over more players. The overall points column shows they are neck-and-neck for ranks 1 and 2.
- The Angels are in this list. Wait a minute! I thought I said the model smooths out the impact of unicorns. Well, it does (not totally, though). The Angels were superior drafters in the early 2000’s. Again, the filterable list results in “Oh, ya, he was good”. Lackey, Rodriguez, Devon White, etc…and Trout.
- Note how the Chicago White Sox do well in the IFA market.
- Note how the Pirates do reasonably well drafting WAR, but less so getting notable players out of that group, and almost non-existent effort in the IFA arena.
- Note that the Seattle Mariners are among the poorest drafting teams, but really good at mining the IFA marketplace. I supposed 1 Ichiro Suzuki helps that. King Felix don’t hurt neither. Or Julio Rodriguez.
There are lots of nuggets and observations one can make from this data. I’m certain I should weight the pointing system different but haven’t yet deduced how to establish weights that would be better.
Next, will move on to the last 10-year view. This is a very volatile list. As noted in other columns, it takes 7-10 years to evaluate a draft. My ten-year view presented below only has 3 years (2014-2016) that can be reasonably considered stable in terms of results. But there are some trends that appear that one might suspect are spot on.
So, what do we see different, and do those things seem reasonable?
- The Angels fall out of the top 20%. This is totally reasonable. Other than signing Ohtani, they really have not drafted or signed anyone notable since 2012.
- The Astros appear in the top 20%. Shocking? Hardly. They went from doormat to dominant in the AL West on the tank-and-rebuild model. They actually started drafting well around 2008, and peaked with the 2015 draft (Bregman, et.al).
- You can’t see it in this summarized dataset, but no one is getting great value from the 2017 draft. More WAR has already been delivered to the MLB from the 2018 draft, in fact.
- Note how many teams have nothing from the IFA market in the last 10 years. Whereas drafted players tend to reach MLB within 5 years or so, IFA players either go directly to MLB (from other pro leagues) or tend to take more like 7 years to reach the MLB, so this dataset is too recent to effectively evaluate IFA signings for the period.
- One might note the relatively low ranking of the Tigers and Royals. This might contradict the popular narrative that these are up-and-coming good young teams. And might explain the Royals forays into last year’s FA market and the Tigers stance this year.
For fun, go pull out a minor league system ranking from 7-10 years ago. Doesn’t matter which one (Law, Dykstra, BA, FG, BP, whichever). Choose your favorite source from between 2014 and 2017. Compare their system rankings from back then (their forecast) to the results you see today (the backcast). Other than the Dodgers, I’d bet their list would be more accurate if it were placed upside down. New system rankings will start to bleed out soon. Should put them in context.