Daily Fantasy Baseball: Investigating Trends in 10-Run Games
I don't know about y'all, but when I'm picking a stack for daily fantasy baseball, I'm not looking for them to slap around some singles and plate five runs. That's enough to get them a win, but it's not enough to send me on an Alaskan cruise to chill with the salmon.
Nah, fam. I'm looking for those puppies to hit double digits.
If you pick a team that scores 10 runs as your stack, there are 10 runs and 10 RBI's available for the taking. That's the zone that can catapult you to the top of the scoreboard and make that fancy steak dinner a little more realistic.
This doesn't mean this is an easy endeavor. A single team scored 10 runs in a game only 298 times in 2015, which is less than two per day. When there are 30 teams in action, the odds of consistently pinpointing which teams blow up aren't overly high.
We can, however, at least increase those odds.
The best way to do this is by looking at trends in 2015's 10-run games. If there were certain traits that consistently showed up in each performance, we would know to look for those things when we're filling out our lineups. This won't always help us nail the right team, but it can at least bridge the gap between randomness and complete predictability.
Let's try to do exactly that. Let's examine each of those 298 games and see what circumstances helped lead to each so that we can understand when a team is on the verge of a breakout, helping us better assemble our stacks for sustainable DFS success.
The Data Examined
When doing this, I wanted to basically just flood a spreadsheet with data so that we had as many potential sources for juiciness as possible. This included weather, the ability of the individual lineup, the ability of the opposing starting pitcher, and the park in which the game was played.
When examining a lineup, I always used the numbers the team posted in 2015 against pitchers of the same handedness of the opposing starter. If Kyle Schwarber is murdering right-handed pitching, but he isn't in the lineup when a lefty is on the mound, we can get a more complete idea of the Chicago Cubs' abilities by looking at their wOBA against exclusively lefties.
The only pitching stats taken for the opponent were those of the starting pitcher. Because this is the only pitcher we know will work in an individual game, he's the only one we should be planning around. You do want to factor in the strength of an opposing bullpen, but ideally, the team you stack will get some hacks against the middle relievers. If you can get a lineup that ushers the opposing starter out early, then the bullpen arms that follow aren't likely to be the team's best. This is why we focused exclusively on the starter.
Finally, park factors and home run park factors were both also considered. All totals were based on the three-year averages for park factors, which you can find in this Google doc. You can find more specific park factors for each individual season on ESPN, though I've found the expanded sample size to be a bit more helpful.
With all of the lame clarifying mumbo jumbo out of the way, let's get to the good stuff. What do the results show us?
We'll start things off just by dumping all of the data into one chart. Then we'll break things down on a more individual level for each category after to provide more complete context.
|Wind Blowing Out||34.9%|
|Wind Blowing In||15.1%|
|Opp. Strikeout Rate||18.4%|
|Opp. Walk Rate||7.5%|
|Home Run Park Factor||1.036|
So much for having a home-field advantage.
This isn't going to draw me away from targeting teams at home. Teams at home in 2015 won 54.12 percent of their games, meaning -- duh -- they scored more runs than the opposing team more than half the time. This means I'd be targeting home teams when I'm stacking in cash. However, when it comes to a tournament, this really doesn't appear to have much of an effect at all.
The wind data here is very similar to what we saw in profiling multi-homer games. Naturally, having the wind blowing out is going to lead to more offense, and the wind blowing in is going to put a wet blanket on things. That shouldn't be surprising.
In a future lesson, when we'll look at high-upside games from pitchers, we'll see that the wind was blowing out 33.58% of the time. This isn't a major deviation from what we saw here, but that is a difference you'll want to note. Additionally, the average wind speed blowing out in the 10-run games was 8.47 miles per hour compared to 8.00 miles per hour for the high-upside pitchers. Bottom line: wind speed matters, and you should be factoring it into your process.
The same is true with temperature. The average temperature in 10-run games was nearly identical to what we saw in the multi-homer games, but it was almost two degrees warmer than the big games from pitchers. If you're deciding between a pair of games, the offense in the warmer temperatures -- especially if there is a significant split -- is likely the superior choice.
When we look at the average wOBA, it doesn't appear superbly impressive compared to a league-average wOBA of .313. It does gain some major relevance, though, when we break things down into tiers.
As a note, the column on the far right shows which teams overall in 2015 fell in each respective range. These were all weighted based on the number of plate appearances for each team. For example, it wouldn't make sense for a team with a .320 wOBA against lefties over 1,200 plate appearances to influence the sample the same amount as one with a .310 wOBA against righties over 4,800 plate appearances. The column on the right essentially shows the percentage of plate appearances that fell within each range to show the odds that a random team on a given day would have a wOBA in that range against that specific handedness.
With this, it means that if a "sample percentage" were higher than the "overall percentage," it means that the marks in that range were more conducive to 10-run games. If there were more teams with a wOBA of .330 or higher that scored 10-plus runs in the sample than overall, then it would mean we should be targeting teams in that range. We can safely assert that this is the case based on the table.
|wOBA||Occurrences||Sample Percentage||Overall Percentage|
|.330 or Higher||36||12.1%||6.6%|
|.320 to .329||70||23.5%||19.7%|
|.310 to .319||104||34.9%||35.2%|
|.300 to .309||57||19.1%||24.3%|
|.299 or Lower||29||9.7%||14.2%|
Teams with wOBA's of .320 or higher only controlled 26.3% of the total plate appearances in 2015, but 35.6% of the 10-run games came from teams at or above that mark. This should be a pretty easy benchmark for us when we're formulating a blueprint for high-upside stacks.
A look at the teams in this sample can further illustrate the importance of wOBA. The Toronto Blue Jays led the league in wOBA against both righties and lefties in 2015 with marks of .341 and .354 respectively. They scored 10 or more runs in a game 26 times as a result. Whew.
The second-most heavily represented team was the Texas Rangers at 17 times. They had a .321 wOBA against righties and .320 against lefties, both of which were in the top 10 in the league. The New York Yankees were also in the top 10 against both types, and they were third with 14 appearances.
Using wOBA by handedness can also help save us from erroneously targeting teams that have heavy platoon splits. The Baltimore Orioles had a .325 wOBA against righties, and they scored double digit runs six times against a righty starter. Their wOBA against lefties dipped to .289, and they only racked up 10 runs once under those conditions.
Despite the Orioles' clubbing of righties, they weren't afraid of strikeouts. They went down on strikes 21.7% of the times, far exceeding the league average of 20.4%. This didn't seem to stop them from putting up runs, though. Was that a universal truth?
The returns here are a bit mixed. While striking out less certainly appear to be more advantageous, it also doesn't appear damning if a team is a bit more third-strike friendly.
|Strikeout Rate||Occurrences||Sample Percentage||Overall Percentage|
|22.0% or Higher||40||13.42%||13.87%|
|20.0% to 21.9%||135||45.30%||50.69%|
|18.0% to 19.9%||91||30.54%||25.51%|
|17.9% or Lower||32||10.74%||9.94%|
It would be hard to completely ignore strikeout rate after looking at this. The important thing -- for me -- comes from looking which teams fit in the bottom end of the range.
The Kansas City Royals didn't have a wOBA above .320 against either righties or lefties in 2015. Yet, they still managed to have 10 double-digit run games. We likely wouldn't have assumed that if we were looking exclusively at wOBA. However, strikeout rate may have helped us still wind up stacking the Royals on occasion.
The Royals had the lowest strikeout rate in the league against both left-handed and right-handed pitchers. This means they were putting more balls in play than any other team in the majors. Even if they weren't necessarily clubbing home runs (they were 24th in the league in dingers), they put enough runners on base that they were able to plate their fair share of runs. Base runners are your best friend, and a low strikeout rate can lead to a whole load of them.
On the flip side, we also don't want to necessarily avoid teams with lofty strikeout rates. The Houston Astros had nine 10-run games against right-handed pitchers despite fanning 23.6% of the time. Because they had big boppers who were extremely all-or-nothing, they could post runs in a hurry without necessarily swimming in base runners. You need a team with a lot of power to compensate for a high strikeout rate, but those teams do exist.
I would be most likely to use strikeout rate to determine in which game type I'd rather use a lineup. The Royals are more cash-game friendly because of the number of balls they put in play, but the Astros present more risk with their propensity for strikeouts. I'd use them in a tournament against a low-strikeout pitcher, but I'd likely forego stacking them almost completely in cash.
Speaking of creating base runners, you can also do this by drawing walks. You would think that if a team is drawing a high volume of walks, they'd be plating a bunch of runners. This isn't incorrect, but the link to 10-run games doesn't appear too terribly strong.
|Walk Rate||Occurrences||Sample Percentage||Overall Percentage|
|9.0% or Higher||37||12.42%||12.08%|
|8.0% to 8.9%||88||29.53%||22.28%|
|7.0% to 7.9%||103||34.56%||37.12%|
|6.9% or Lower||70||23.49%||28.52%|
More often than not, a team excelling here is going to have a high wOBA. Having a high walk rate isn't a bad thing, but it's not something I will value as highly as wOBA.
I'll factor walk rate most heavily into the equation when a team with a high walk rate is facing a pitcher with a high walk rate. If I can get a team that draws a bunch of walks against a guy who can't sniff the strike zone, then the effect of those extra base runners will be amplified. Outside of that, I'll generally defer to wOBA instead.
This brings us to a look at the opposing starters. Right now, it seems as if using wOBA can provide us a solid idea of which teams are going to excel, though it doesn't quite feel complete. The numbers of the opposing starter really ease any concerns.
As with all discussions of pitcher competence, we'll start with SIERA. This is an ERA estimator that accounts for walks, strikeouts, batted balls, hair flow, and pretty much anything else that dictates a pitcher's performance. It's the number I turn to first when deciding which pitchers to stack against.
Based on the table below, that looks like a solid decision. As the SIERA rises, so, too, should your level of excitement.
|Opp. SIERA||Occurrences||Sample Percentage||Overall Percentage|
|3.29 or Lower||27||9.06%||13.53%|
|3.30 to 3.59||20||6.71%||10.53%|
|3.60 to 3.89||33||11.07%||12.78%|
|3.90 to 4.19||59||19.80%||28.57%|
|4.20 to 4.49||64||21.48%||16.54%|
|4.50 or Higher||95||31.88%||18.05%|
There may not be a ton of pitchers who have SIERA's of 4.20 or higher, but they are infallible saints in our eyes.
In this instance, the "overall percentage" column shows the percentage of starters in 2015 who threw at least 100 innings and fit in that respective range. Over half of the games came from pitchers with SIERA's of 4.20 or higher, though only 34.59% of overall pitchers hit that mark.
The dropoff after that point is fairly steep. More often than not, you're going to have at least a few games on a given slate in which a starter has a SIERA of 4.20 or higher. If you can find that, then you should try to take advantage. If not, then you might need to start digging elsewhere. This, though, is where you should at least start your opponent research.
As mentioned, both strikeout rate and walk rate are cooked into SIERA. However, they can still provide us with a bit of help in formulating our selections on those slates in which SIERA is less helpful. Strikeout rate is the aspect that appears most definitive.
|Strikeout Rate||Occurrences||Sample Percentage||Overall Percentage|
|25.0% or Higher||19||6.38%||12.78%|
|22.0% to 24.9%||48||16.11%||16.54%|
|19.0% to 21.9%||55||18.46%||28.57%|
|16.0% to 18.9%||90||30.20%||22.56%|
|13.0% to 15.9%||47||15.77%||15.04%|
|12.9% or Lower||39||13.09%||4.51%|
Don't target guys with crazy high strikeout rates, a'ight?
The safe zone here appears to be once the strikeout rate dips below 19.0%. Considering the league average for starters in 2015 was 19.5%, this shouldn't be a huge shock. The advantage spikes against guys with strikeout rates below 13.0%.
A lot of times, these players are going to be guys who will quickly work their way out of the rotation. After all, only six pitchers in 2015 had a strikeout rate of 12.9% or lower and managed to throw at least 100 innings as a starter. This is why you'll need to be able to assess when a guy is going to fit in the category before he gets the boot.
If you see a player getting a spot start, check out his numbers either in the bullpen or in the minors. The average strikeout rate for relievers in 2015 was 22.1%, meaning you should expect a fairly sizable dip when a guy moves into the rotation. The same would likely be true for their minor league numbers. If these marks are below average for their situation, that's when you'll want to spring while you still can.
We don't see as strong of a spot for exploitation when it comes to walk rates. Pitchers with above average walk rates do seem more likely to allow big games, but not significantly so.
|Opp. Walk Rate||Occurrences||Sample Percentage||Overall Percentage|
|9.0% or Higher||55||18.46%||13.53%|
|8.0% to 8.9%||54||18.12%||18.05%|
|7.0% to 7.9%||75||25.17%||20.30%|
|6.0% to 6.9%||42||14.09%||16.54%|
|5.0% to 5.9%||40||13.42%||19.55%|
|4.9% or Lower||32||10.74%||12.03%|
If a guy makes up for a high walk rate with a hefty number of strikeouts, then he's likely not the best to target. This is why I would make this a very much secondary concern behind SIERA and strikeout rate. However, it does still have a place.
Let's take the Cleveland Indians' Trevor Bauer as an example. In 2015, he had a 22.9% strikeout rate, well above our general target zone. However, it's still not an overwhelming number.
He coupled that with a 10.6% walk rate. As you can see above, that's a zone where things start to look decently optimistic for the opposing offense. Bauer also was a heavy fly-ball pitcher with a 39.2% ground-ball rate, meaning those mistakes with the walks could be amplified in a hurry. This is why he would be a good candidate to stack against.
I'm not going to target a guy exclusively because of a high walk rate. This would change, though, if he added either a below-average strikeout rate or ground-ball rate. If he can check two of those three boxes, then he's either going to allow a ton of base runners or get pounded when he makes mistakes. Those are both huge for our purposes. Even though the walk rate by itself may not help us a lot, it can do so when we view it in a broader context.
We've seen thus far that both the individual offense and the opposing starter both play a role in predicting when a team will spout off for 10-plus runs. If you can find the two together, then you'd seem to be well on the road picking yourself a winning stack. Put both of those elements in a good park, and you're cooking with gas.
Because it would make most sense that 10-run games would occur in games with high park factors, we'll start there before looking at home run park factors. I weigh both into my decision-making, though they serve different purposes. Regular run park factor is my favored category for stacking.
Here's the breakdown by park factor. The league-average park factor is 1.000, but our sweet spot appears to skew a bit above that.
|Park Factor||Occurrences||Sample Percentage||Overall Percentage|
|1.050 or Higher||90||30.20%||20.00%|
|1.020 to 1.049||82||27.52%||26.67%|
|0.950 to 1.019||70||23.49%||26.67%|
|0.949 or Lower||56||18.79%||26.67%|
Only 46.67% of the games took place in parks with a park factor of 1.020 or higher, but they accounted for 57.72% of the 10-run games. This effect gets even larger as the park factor increases.
The shining pinnacle of park factor Gucciness is, of course, Coors Field in the thin Colorado air. This one park produced 23 different 10-run games in 2015. This makes the park by itself nearly as productive as the Blue Jays, who had far and away the best offense in the league. Target Coors with regularity.
Players who go to Coors -- both on the Colorado Rockies and their opponents -- are going to see price hikes. However, that doesn't mean you can't find value. The value usually comes in the batters hitting fifth or lower in the order, and we've seen previously that these guys are definitely in play when it comes to tournaments. They're also likely to carry lower ownership, making middle-of-the-order stacks at Coors endlessly fun on a regular basis.
It would also be smart to look at parks directly beneath Coors. Offenses here don't see as big of a boost in value, but they also don't see the same increases in price and ownership. If you can snag a high-powered offense in Boston, Baltimore, or Milwaukee, you'll likely be sitting pretty. Those three parks had 42 different 10-run games in 2015. Look to them when you need some salary relief from Coors.
We don't necessarily see the same effects when it comes to home run park factors. This would make sense as this is less directly tied to runs scored, but there really doesn't seem to be much here.
|Home Run Park Factor||Occurrences||Sample Percentage||Overall Percentage|
|1.200 or Higher||95||31.88%||23.33%|
|1.000 to 1.199||54||18.12%||23.33%|
|0.900 to 0.999||73||24.50%||23.33%|
|0.899 or Lower||76||25.50%||30.00%|
Upon first glance, it would look as if that top zone with home run park factors of 1.200 or higher would be conducive to big games. That's not untrue, but all of the parks in that zone also had above-average run park factors, with only Yankee Stadium sitting below one of our top two rungs in the park factor table.
On the flip side, Fenway Park falls to 24th in home run park factor. There were 18 double-digit run games there in 2015, so we clearly shouldn't be disregarding it simply because of a depressed home run park factor. This is why I'll spend almost all of my energy on park factor as opposed to home run park factor.
Constructing a Blueprint
Now that we've seen all of the data, let's paste this together in order to know what to look for in our stacks on a daily basis. What are the benchmarks we want to hit in each category?
We'd prefer to have a game where the wind is blowing out and the temperatures are high. Calling these categories "tie-breakers" would be a disservice to their importance, and I value them more highly than that due to the discrepancies in the data we found. You should be looking at weather as you weigh your lineup decisions.
The sweet spot for an offense is one that has a wOBA of .320 or higher against pitchers of the same handedness of the opposing starter. We can dip a bit below that if the team has a low strikeout rate, but our tournament stacks likely need a lofty wOBA to post the point totals we're looking for.
Strikeout rates and walk rates are both well behind wOBA in our considerations, but they both do serve a purpose. Teams with low strikeout rates are likely safer, and a team with a large walk rate facing an erratic pitcher could lead to a high volume of base runners. We shouldn't allow these marks to overpower wOBA, but they should at least be a part of our process.
With the opponents, the higher the SIERA of the opposing starter, the higher our interest should be. The ideal spot we want is at least 4.20, though the pitchers with SIERA's above 4.50 are especially accommodating to opposing offenses. When you can find these guys, they should be on your list to target almost regardless of the rest of the situation.
We are looking -- for the most part -- to avoid pitchers with strikeout rates north of 19.0%. That's where we see a steep fall-off in 10-run games. We can find exceptions, though, in guys who have high walk rates and low ground-ball rates. If we can check two of those three boxes between a low strikeout rate, high walk rate, and low ground-ball rate, we'll likely be doing okay.
Finally, with park factors, we want to focus mostly on strict run park factors. Home run park factors are more useful when looking for individual players in a tournament rather than a stack as it could lead us to exclude from consideration parks that are ripe for the picking. The sweet spot here is any park with a park factor of 1.020 or higher, though there is a considerable jump once we get above 1.050.
We're not always going to be able to find games that will fit under all of these categories. That said, of the 298 games on our list, 41 of them featured a team with a wOBA of .320 or higher facing a pitcher with at least a 4.20 SIERA with a strikeout rate below 19.0% in a park with a park factor of at least 1.020. That's a significant number of boxes to check, and having that many games fill each of the criteria should tell you a significant amount about the importance of these benchmarks.
Once you've been doing this for a while, the process will become more natural to you. You won't need to intently study each and every stat of all teams on the slate as you'll already know which teams rake against lefties. But incorporating these numbers into your daily research as you seek out the most fruitful stacks is a strategy that can help you nail your stacks more often than you would otherwise.
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