Greetings and salutations, fellow baseball researchers. It is I, Mr. Moving Averages back at the helm as we charge once more through sports and time in our exploration to dissect the inner workings of the Quality Start. We have come so far in such a short period of time. If any of the references or stats in this article appear to lack context, please refer back to prior segments for their basis, creation or explanation. There’s a lot of information and results we have established to get to this point, so looking back for a refresher is always understandable; We’ve introduced several new processes and statistics. We’ve confirmed our assertions and finally, the data mining is done; Let the analysis begin! In Part 8, the fantasy rubber meets the road as we apply some of our research to actual ballplayers that will help us to take home the fantasy trifecta; The cheddar, the chip and the trophy. We came into this with a purpose, and once this collective of truth seekers climbs the top rope you know the big elbow isn’t far behind (RIP Randy Savage OOOOH YEAAAA).
We have already successfully established correlative links between Quality Starts and finish rank by position in point leagues, and although they were a touch weaker than initially understood, the charts and underlying statistics still vehemently agreed. We then discovered in 2018 a starting pitcher who achieved a baseline QS by Distance/Run Outcome (6+/3 D/RO) converted that start into a win roughly 55% of the time. Before we go any further using that number as a base for assumption, I decided to run the conversion rate formula for the entire game sets of both 2016 & 2017 for comparison, and hopefully validation. If indeed the Quality Start Win Conversion % holds somewhat constant for two other samples of over 2000 starts each, then I think we have a pretty solid foundation for our adjusted win expectancy.
- In 2016, there were a total of 2,263 Quality Starts thrown. Of those, 1231 were converted to wins for a QSWC% of 54.3%
- In 2017, there were a total of 2,126 Quality Starts thrown. Of those, 1199 were converted to wins for a QSWC% of 56.3%
- If we average the entire set of games from 2016-2018 (6,385 QS), 3,523 were converted to wins for a QSWC% of… (Drumroll please…) 55.2%! YES!
Such a tremendous sample yielding such consistent results tells me we have a pretty good statistical base to build our foundation for late pitcher pairs & streamers. All pitchers listed below have heavily slanted home/road splits and are candidates for either; I think deciding which just depends on league format. An owner who drafted hitters early in a deep league may be looking to combine two different starters to create the equivalent of a pitcher drafted much higher. In a more shallow league, these pitchers are attractive streaming options; All guys with a high expected rate of QS achievement at home, which of course we also hope will translate into wins on top of the Quality Start. Let’s get this party started with SPs outside of the top 50 by ADP who went for a QS in +66% of their 2018 home starts, but whose road numbers dragged down the overall stats to the point they are being overlooked. Only 9 starters fit this profile, and they currently have an average ADP of 475.15. I love fun stats.
KEY:
- QS – Total QS in 2018
- hGS – Home Games Started in 2018
- hQS – Home Quality Starts in 2018
- hQS% – Home Quality Starts as a % of Home Games Started in 2018
- ADP – Cited from FantraxHQ on 3/13/19
| NAME | QS | GS | hGS | hQS | hQS% | ADP |
|---|---|---|---|---|---|---|
| Borucki, Ryan TOR | 11 | 17.00 | 7 | 6 | 85.71% | 489.87 |
| Peralta, Freddy MIL | 6 | 14.00 | 5 | 4 | 80.00% | 280.18 |
| Giolito, Lucas CWS | 15 | 32.00 | 15 | 12 | 80.00% | 370.32 |
| Anderson, Brett OAK | 6 | 17.00 | 5 | 4 | 80.00% | 913.97 |
| Ryu, Hyun-Jin LAD | 9 | 15.00 | 9 | 7 | 77.78% | 192.59 |
| Rodriguez, Dereck SF | 14 | 19.00 | 13 | 9 | 69.23% | 286.14 |
| Chen, Wei-Yin MIA | 10 | 26.00 | 13 | 9 | 69.23% | 543.87 |
| Buchholz, Clay ARI | 10 | 16.00 | 6 | 4 | 66.67% | 413.37 |
| Richard, Clayton TOR | 12 | 27.00 | 12 | 8 | 66.67% | 786.03 |
That’s an entire crop of pitchers (however anesthetically unappealing) that basically no one is interested in. Combine this utter disgust with our prior research on the effect of a top 10 bullpen and/or a +.580 team win team percentage and there you have it; A few guys that should be atop our pitcher pair/streaming list (WHEN THEY’RE AT HOME). I’m going to keep these guys on the scout team for use in a pinch. Sometimes, especially in point leagues a week begins with a large discrepancy in starts for that particular matchup. If you find yourself outgunned on any given week and any of these pitchers are going twice at home in a week, there’s a reason for interest. I’ll add a personal anecdote that I am not proud of, but it did work. I started Homer Bailey 2x in a critical semifinal playoff game, and it made the difference between winning and losing. If you ever repeat that to anyone, our friendship is over.
As I dove into these home/road splits to look for outliers, I found something of note worth sharing. When I first set out on this exercise I expected to have a list of pitchers that excelled at home, and another for pitchers who excelled on the road. Upon further examination, it turns out that there isn’t a single starter in our set that is both outside the top 50 SP and had a +66% Quality Start percentage on the road. There goes that idea. It’s not exactly what we were searching for initially, but since I did mine the data I figured I would share the best road pitchers by QS% in 2018. It’s not going to help identify streamers, but it useful info and may play a factor into your decisions come draft day.
| NAME | GS | aGS | aQS | aQS% |
|---|---|---|---|---|
| Strasburg, Stephen WSH | 22.00 | 10 | 9 | 90.00% |
| deGrom, Jacob NYM | 32.00 | 16 | 14 | 87.50% |
| Verlander, Justin HOU | 34.00 | 15 | 13 | 86.67% |
| Kershaw, Clayton LAD | 26.00 | 13 | 11 | 84.62% |
| Kluber, Corey CLE | 33.00 | 16 | 12 | 75.00% |
| Nola, Aaron PHI | 33.00 | 16 | 12 | 75.00% |
| Sale, Chris BOS | 27.00 | 15 | 11 | 73.33% |
| Taillon, Jameson PIT | 32.00 | 18 | 13 | 72.22% |
| Scherzer, Max WSH | 33.00 | 14 | 10 | 71.43% |
| Marquez, German COL | 33.00 | 17 | 12 | 70.59% |
| Carrasco, Carlos CLE | 30.00 | 17 | 12 | 70.59% |
| Freeland, Kyle COL | 33.00 | 18 | 12 | 66.67% |
| Bauer, Trevor CLE | 27.00 | 15 | 10 | 66.67% |
| Syndergaard, Noah NYM | 25.00 | 12 | 8 | 66.67% |
| Happ, J.A. NYY | 31.00 | 12 | 8 | 66.67% |
Our first application for maximizing QS% data and its expectations gave us more than just a list of pitchers being undervalued when they take the bump at home. I think it’s important to not overlook another critical takeaway here; The difficulty in consistently pitching well on the road. It’s something that many might assume, but I always find it useful to quantify these issues when possible. This conclusion is definitely something to consider before even choosing to take the streamer strategy in point leagues. Personally, since the majority of point leagues are essentially centered around OBP or OPS I like to fill my staff with high end starting pitchers that can deliver dominating single game performances and then collect value hitters with consistent outputs. This next step is the launchpad where we take this endeavor to the next level. How do we quantify the varying degrees of Quality Starts if they are all counted the same? We now know that these starts of varying D/ROs are not equal; There is a significant spike in Quality Start Win Conversion Percentage in the highest tiers. Is there a metric for stratifying the QS to identify starters with the highest amount or highest rate of dominant outings? The answer is no, and here we go again. We will never accept a sub par calculation of the stats we need to achieve our goals of fantasy glory, so I’m going to develop, define, collect and sort and entirely custom QS database to fit our needs that an entire industry has ignored. Part 9 will showcase the piece de resistance; A new perspective and quantifiable determinant of an individual start’s success using the Distance/Run Outcomes (D/RO) we’ve mined and analyzed in Part 7. This is incredibly exciting for me, for us, and maybe the entire point playing community. I’ve been grinding day and night to define, cultivate and sort these new tiered Quality Starts; So stay tuned for Part 9 and make sure to lobby Grey for a guest spot on a pod & send your local sabermetrician my way for some recognition.
I’ve got babies to feed, stats to mine and drafts to draft. Please follow, share, like, comment, yell, kick, scream or just about anything else to let us know you enjoy the work and where it’s going. It’s really been an honor and a pleasure to share this genuinely immersive experience with you all. Follow @MLBMovingAvg and don’t miss a thing! Remember, calculate the risk reward ratio in all your decisions; Life is a fantasy draft.
