Recent callup Bo Bichette (No. 8) has crushed major league pitching to the tune of a 238 wRC+ with 13 XBH in just 53 PA. Trent Grisham (No. 6) and Isan Diaz (No. 54) have also shown promise in their brief appearances and look to make an impact over the rest of the season. There’s plenty of other movements in the rankings, especially towards the bottom of the list where the projections are a virtual tie that can be broken by recent performance. The methodology of the system for projecting peak WAR can be found here, and more detailed projections with percentile outcomes beyond the top 100 can be found in the link here.
Player Notes
- Gavin Lux continues to demolish minor league pitching, batting .481 in the last week with a home run and two SB to bring his AAA slash line to .456/.535/.846 overall. He gets the edge over Luis Robert at No. 2 due to better plate discipline, but Robert’s been no slouch in AAA either.
- Also climbing is Jarred Kelenic, who moves up to No. 13. After starting off relatively slow in A+, he’s cut his strikeout rate down to 19.2% over the last two weeks with a 166 wRC+ overall in that time span. It’s a great sign to see Kelenic making improvements after striking out 27.7% of the time prior to that in A+.
- Daulton Varsho has an interesting profile for a catcher, averaging 21 SB per 450 PA in the minor leagues. He’s also continued to hit for power with 14 home runs in AA this year to go along with a 13.2% strikeout rate. He’s become one of the premier catching prospects in baseball.
- Mickey Moniak’s stats may be somewhat inflated by his home ballpark, but it’s still been a comeback season for the former No. 1 overall pick. His stock is trending up due to his age relative to AA and overall well-rounded performance.
- Between AA and AAA, Kevin Padlo has hit 18 HR with 12 SB and a 158 wRC+ overall in 351 PA. He’s also only just turned 23. The main flaw in his profile is a 25.9% strikeout rate, but he also carries a 16.2% walk rate. The Rays infield is crowded enough as it is, but Padlo has an interesting enough blend of power and speed to warrant stashing in deep OBP leagues.
- Ryan Vilade gets a small bump in the projections for batting .375 with 3 HR over the last week. He doesn’t clearly stand out in any one area, but he’s stuck at SS with some power and speed while maintaining sub-20% strikeout rates.
- Geraldo Perdomo got the call to A+ and has continued to hit, batting .381 with 3 SB and an 8.3% strikeout rate over 24 PA. He’s shown impressive speed and plate discipline as a 19-year-old SS.
- Recent trade acquisition Joshua Rojas has been on a tear for the Diamondbacks AAA affiliate. It’s just 34 PA, but Rojas has a .586/.647/1.000 slash line with an 8.8% strikeout rate. He’s been excellent at every level in 2019 and could get a shot in the major leagues soon.
- Since his promotion to AA, Alec Bohm has 11 HR in just 174 PA while impressively maintaining a 13.8% strikeout rate. His combination of contact ability and raw power is built for today’s MLB.
- At just 18 years old, it’s impressive enough that Julio Rodriguez is playing in A-ball, but over the past two weeks, he’s batting .320 with 3 HR and a 17.9% strikeout rate. Rodriguez now carries a 138 wRC+ at the level and continues to trend upwards.
(Photo by Michael Wade/Icon Sportswire)
No juicy Wander Franco updates?
What do you think of the fact that Bichette has grossly outperformed what he did in AAA this year and AA last year? We can’t pretend that he developed on the way to the stadium, so what does it really tell us? I think it is a pretty damning indication about the quality of baseball being played at the MLB level… well, that and some unsustainability but what is happening in MLB should at least draw some attention and I don’t mean that in a positive way.
I mentioned this a few weeks ago, but lose the xWAR – it is worthless. It is going to look really bad in the future and it doesn’t tell you anything today. There is no predicting prospect futures with any accuracy for so many reasons – Fangraphs has been making no progress for quite some time. The evidence is already there – even though nobody ever acknowledges its futility. Put another way, I am good at ranking prospects and I laugh those projections off. I really do believe that failure to acknowledge that this is futile sets us back. Its like there is ultimately no concern with accuracy, just in creating more models. You could replace that column with level and organization – it would tell you a lot more. Their raw stats would tell you a lot, lot more. WAR is the least appropriate thing to project as it incorporates defense and base-running and you literally have nothing work with of any value in minor league data. Just posting their league adjusted performance would be good but even those “normalizations” are witchcraft. Even if you want to rank them off of projected WAR (the idea that you are also projecting peak is even more flawed), you don’t have to list it – it doesn’t tell anyone anything.
Thanks for reading. I don’t think the results are as bleak as you suggest — I’ve found there is predictive value from backtesting historically, especially in the higher levels of the minor leagues. I’m not saying this is going to be the single most accurate list out there, but it’s going to highlight some interesting players who may be passed over for any particular reason. It’s also going to be one of the most unique lists out there, which can be useful for identifying potentially undervalued prospects.
As for choosing 3-year peak WAR as the output, I go into more detail in the original article in this series, but I found it struck a good combination of comprehensive value and avoiding noise in playing time that low ranked prospects in particular face as well as injuries. 1-year peak WAR was too small of a sample and total WAR or WAR over a larger span scored lower in the model outputs because of the latter factors. I agree that defense is probably the biggest blind spot right now, but I have ideas to improve on that in the future.
Since the models use logistic regressions, xWAR is calculated as the weighted mean of the probabilities of each WAR bucket, so it’s better to think of those as average outcomes rather than exact projections. I’ll be publishing more on the accuracy in the future, but I do believe it’s sufficient to be useful.