The 2018 Pod Projections are now available! For the first time, the package includes NFBC ADP, along with all historical Pod-developed xMetrics. My projections are based on the methodology shared in my eBook Projecting X 2.0, and the process continues to evolve and improve (thanks Statcast!). Given the hype and the difficulties of translating performance from a foreign league, it was obvious who the first player for this series should be — Japanese uber-athlete Shohei Ohtani.
Although I do include a batting forecast for Ohtani in my projections, we’re not going to discuss them here. He’s not expected to garner enough plate appearances to accumulate much value at the plate. Instead, we’ll stick to his pitching, which is no doubt what has driven his NFBC ADP of 75.4.
Projections are hard. It’s challenging enough to just project a veteran Major League player from one year to the next. It’s even more challenging to project a minor leaguer making his first MLB appearance. Furthest up the totem pole might very well be projecting foreign players in their first taste of action in the States, whether coming from Cuba, Japan, or Korea. So if most projections are well-educated guesses, foreign player forecasts are more like just educated guesses.
My process for Ohtani, and all foreign players if the data is available, is to collect his underlying metrics, index them to his league’s average, then translate those indexed metrics to MLB to yield an equivalent mark. From there, perhaps make some minor adjustments to account for uncertainty of a perfect conversion.
Luckily, I was recently introduced to a website (check the English box on the right side above the stat table) that displays FanGraphs stats for Japanese players. Jackpot! Rather than have to somehow convert an ERA, I can now project the underlying skills and use the same process for current Major Leaguers. The stat categories should look familiar, and they even include batted ball type distribution (LD%, GB%, FB%), as well as BABIP (labeled as DER, it’s 1 minus DER for BABIP) and HR/FB rate all under the Batted Ball tab. Unfortunately, without a paid subscription, you are unable to access everything the site offers, but there is enough for free to accomplish what I needed to.
Even with all this data, there is still much more that should be accounted for, but I don’t have the data for and have not. This includes park factors and the quality of his defensive support.
So let’s get to it. As a reminder, I do not forecast ERA or WHIP manually, but rather the underlying skills those drive the results like K%, BB%, GB%. Excel then throws those inputs into my various formulas and an ERA, WHIP, HR/9, W-L record, etc, are calculated.
Games Started | IP: 24 | 150
In his time in Japan, Ohtani never started more than 24 games or pitched more than 160.2 innings. I’m guessing the Angels are going to want to protect their investment and not push Ohtani to do something he has never done before. So I’m going with 24 games and 150 innings for now, which equates to 6.3 innings per start.
One of the reasons we’re so excited about Ohtani is his history of big strikeout rates. In 2015, he averaged nearly 95 miles per hour with his four-seam fastball (he threw it nearly 57% of the time), and complemented the heat with a splitter (almost 22% of the time) and slider (17% of the time). Both splitters and sliders induce gobs of swinging strikes, so it’s pretty clear how he has managed such strong strikeout rates. Let’s take a look at his historical strikeout rate marks, compare them to league average, and then convert them to MLB.
|Season||Ohtani K%||Lg Avg K%||K%+||K% Indexed to 20.5% AL SP Lg Avg|
In Japan, players put the bat on the ball more frequently than here, as evidenced by the Pacific League K% average sitting below ours, though we do see a sudden spike in 2017. Even so, Ohtani has still managed to remain a strikeout machine, and those marks look even more impressive after indexing them. Then once converting them to MLB marks, you find just once would his K% have dipped below 30%, and that came in a small sample of just 25.1 innings.
With those numbers, I decided to be conservative and settle on a 27.7% strikeout rate. It would be shocking if Ohtani wasn’t among the league leaders in strikeout rate, but with so much still unknown, it wouldn’t truly be a 50th percentile type projection if it was around the 30% mark or marginally lower.
While we know he should rack of the strikeouts, we’re a little more unsure of how his control is going to translate. Nothing suggests he has pinpoint control, but he hasn’t exactly struggled for years with his walk rate either. Instead, it looks fairly average. Let’s check the numbers.
|Season||Ohtani BB%||Lg Avg BB%||BB%+*||BB% Indexed to 8.0% AL SP Lg Avg|
I’m not sure what happened during the last season he pitched, but overall, Ohtani has been a bit worse than the league in walk rate. With the assumption that better Major League hitters are going to lay off his splitter more often than hitters in Japan, I projected a 9.4% walk rate.
GB%/LD%/FB%: 48% / 20% / 32%
Perhaps the least talked about skill of Ohtani’s is his ability to generate ground balls. Strikeouts and grounders?! I was only able to collected his rates from 2014 and 2015, so let’s look at those.
|Season||Ohtani GB%||Lg Avg GB%||GB%+||GB% Indexed to 42.8% AL SP Lg Avg|
So overall, Japanese pitchers induce more grounders than in MLB, which makes Ohtani’s worm-killing capabilities just a bit less impressive. Still, it translates to a nearly 50% stateside GB%, which is excellent. As usual, I took the conservative route and reduced his projected GB% slightly to 48%. I then gave him around a league average line drive rate, which resulted in a better than average 32% fly ball rate.
In all these years, we still haven’t figured out exactly how some pitchers have been able to consistently suppress/boost home runs on the fly balls they allow, outside of park factors. So I generally heavily regress toward the mean, which was 14.1% among AL starting pitchers in 2017. This regression is especially considered when forecasting young pitchers with limited MLB track records or rookies. Like with GB%, I was only able to find his 2014 and 2015 marks, so let’s see how Ohtani performed then.
|Season||Ohtani HR/FB||Lg Avg HR/FB||HR/FB+*||HR/FB Indexed to 14.1% AL SP Lg Avg|
If you solely looked at Ohtani’s HR/FB rate in these two seasons, you would think he was an expert home run suppressor. But when you compare it to the league average, you realize that Japan is simply nowhere near as homer-happy as players here are. They use a more contact-oriented approach, which is why strikeouts are lower and so are home runs. Still, his marks translate to a HR/FB rate just into the double digits, and well below the AL league average.
At the risk of sounding like a broken record, I couldn’t just leave the translated average and call it my projection. So up we go, landing on 12.5%. You have to think that we’ve reached peak HR/FB rate and even if you think this is mostly sustainable, there will be at least some regression. That, plus you need to give Ohtani at least some credit for his home run suppression results, acknowledging the possibility that those skills will carry over here.
Lastly, we finish off with BABIP, which I usually rely on a mixture of batted ball mix, expected defensive support, and history, to guide my projection. Let’s see what Ohtani has done in Japan on the BABIP front.
|Season||Ohtani BABIP||Lg Avg BABIP||BABIP+*||BABIP Indexed to .297 AL SP Lg Avg|
Probably because of the focus on contact and slap-hitting, BABIP is higher in Japan. Ohtani actually underperformed the league in 2014, but was well better than average the following season. Overall, he points to possible real BABIP suppression skills. Or just a sample sample of randomness. Whatever it is, I wasn’t going to forecast a pitcher with a slight ground ball tilt for a .282 BABIP.
That said, the Angels infield defense is expected to be really good, with everyone at least above average, and the left side superb (assuming Zack Cozart smoothly adjusts to third base). So I went with a .290 BABIP given the possibility he owns some real BABIP suppression skills, plus that potentially elite Angels infield.
Below is my final projected pitching line, along with the other systems for comparison:
It’s pretty surprising to me that I’m actually most bullish on Ohtani’s ERA, though the WHIP is right in the middle of the pack. The likely reason? Check the HR/9 projection. I’m second lowest to ZiPS, and that’s a direct result of my ground ball rate forecast. Steamer’s GB% is just 43.2%, which is essentially league average. But we know he was a groundballer in Japan. So those extra homers the other systems are forecasting are hurting his ERA, but not his WHIP. Surprisingly, Steamer is way out ahead in strikeout rate, with everyone else within a narrow range, and we’re all in the same ballpark for walk rate.
Because he probably won’t pitch a ton of innings, but those innings should be high quality, he’s probably more desirable in shallower leagues where replacement level is high. It’s the same philosophy with injury prone players as well. However, until I run my values, I have no clear insight as to whether the 75th overall pick is fair value. It surely wouldn’t seem cheap, but he doesn’t seem grossly overvalued either.
Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year. He produces player projections using his own forecasting system and is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. His projections helped him win the inaugural 2013 Tout Wars mixed draft league. Follow Mike on Twitter @MikePodhorzer and contact him via email.