With fantasy owner always searching for an edge, I may have found one buried deep in spring training stats. While looking for Yonder Alonso’s MLB.com player page, I noticed he had some batted ball data in the form of GO/AO (groundouts/air outs). In Alonso’s case, his GO/AO value had always been greater than 1.1 until last season when it dropped to 0.87. In the regular season, Yonder’s groundball rate plummeted from 44% to 34%. Yonder admitted to making a swing adjustment and that change should be detectable in spring training. By comparing spring batted ball data, fantasy owners can get an idea of those hitters who may be ready for the flyball revolution.

Note: While flyball and line drive rates are available for comparison, I will only use groundball rate (GB%) because it stabilizes quicker, has less stringer bias (tough call between some line drives and flyballs), and is only one set of benchmarks to memorize.

I’m going to have two foci for this article. Part 1 is all math and disclaimers. It’s the process I used to go from GO/AO values to ground ball rates to launch angles. Part 2 contains the results from Part 1 as a simple procedure for finding launch angle breakouts.

Part 1: Math and Disclaimers Read the rest of this entry »

Well, I used to be able to create starting pitcher disabled list (DL) projections with the average chance hovering around 40%. Until this past season when DL estimate was off by 18%.

Even though the rates have climbed the past couple of seasons, it was nothing like the jump this past season and I 100% blame the 10-day DL. All my work to this point is moot.

Read the rest of this entry »

Quite a bit has been written about batted ball data the past few seasons since the information has become publicly available. Fantasy owners have taken notice and are trying to find that next hitter who is raising his launch angle to be part of the “Flyball Revolution”. One major issue which is not being publicly discussed is the major effects backspin has on the ball. By knowing a hit’s spin rate, some of the anomalies seen between launch angle and exit velocity can be explained. The spin rate is a major batted ball component but is generally an unknown factor.

The importance of batted ball spin comes down to this simple table and explanation by Dr. Alan Nathan in a piece he wrote at the Hardball Times.

Finally, I want to take advantage of the fact that we have an aerodynamic model that accounts for most of the features of the data to investigate how flyball distance depends on the amount of backspin, here for a fixed exit speed of 103 mph and launch angle of 27 degrees. The results are given in the table below. They show that distance increases rapidly as the backspin increases from zero but eventually saturates, with very little gain in distance for spin rates exceeding about 1,500 rpm. The reason for the saturation is partly because air drag increases with increasing spin, essentially canceling the increase in lift.

Same launch angle. Same exit velocity. And the ball travels an additional 64 feet of distance because of backspin. Simply, how is a factor which can add an additional 60+ feet in travel distance not be part of our analysis?

Read the rest of this entry »

While I normally examine pitchers in-season, a couple of late-season call-ups I missed deserved some analysis.

Note: All breaks are from the pitcher’s perspective.

Luiz Gohara

The 21-year-old lefty got my attention by showing up as the 38th ranked starting pitcher by our auction calculator. If I’m going to take a guy to be my #3 or #4 starter, I should know a bit about him. Here is my take on his last start of the 2017 season against the Marlins.

• He throws straight to home with no funky angles from a ¾ arm slot and doesn’t fall off the mound after his windup. He shows a little more effort from the stretch. With his simple delivery, he’ll not have as much of a platoon split.

Read the rest of this entry »

Gazing right back at you is someone who relentlessly falls prey to the law of small numbers; to hindsight bias; to over-reaction; to narrow framing; to mental accounting; to status-quo bias; to the inability to evaluate your own future regret; and, most of all, to overconfidence. – Jason Zweig

I’ve been digesting all the literature on behavioral economics I can find as I stated in an article earlier this week. This focus area has gotten quite a bit of attention in the past few years with Richard Thaler and Daniel Kahneman each winning the Nobel Prize in economics for their work in this field. Thaler (Misbehaving) and Kahneman (Thinking Fast and Slow) have each written must-read books on the subject. I recommend both books, they are loaded with information. To give a brief induction into the various behavioral economic concepts and their application to fantasy baseball, I will utilize a speech given by the Wall Street Journal’s Jason Zweig on the subject.

Read the rest of this entry »

Fantasy baseball slowly evolves and with the start of 2018, I’m going to focus on some topics in which savvy and unconventional owners could utilize against their competition. Some of these topics I’ve been contemplating and researching for a while, others are still just dust in the air. The following contains some ideas I hope to have a better understanding of by the year’s end and frame it so owners and utilize it. I’m not the only source of ideas. I’m 100% positive I’ve missed some simple useful topics and would love to research those in presented in the comments.

StatCast Batted Ball Data

As a baseball community, we’ve been slow to adapt and utilize StatCast batted ball information. Besides major league baseball employees, no one is leading the change. Part of the problem is having an easy to access database for everyone to use. That barrier is being removed as tools like Bill Petti’s baseball-r package can be used to help scrape the needed information from Baseball Savant.

Read the rest of this entry »

I’m nearly done with a 15-team, 50-player draft-and-hold league. To say the least, the pickings are slim at this point in the draft. Players I like to concentrate at this point are high talent middle relievers who could close. I can either use them for their rate stats or hope one eventually gets some Saves. Here are pitchers whose projections, especially the strikeouts, I find intriguing.

Josh Hader
Projection
11.6 K/9
4.2 BB/9
3.74 ERA

Read the rest of this entry »

“… pros were more likely to ride a wave of irrational exuberance than to fight it. One reason is that it is risky to be a contrarian. ‘Worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally’” – Richard Thaler in Misbehaving

At the root level, fantasy baseball is about acquiring more undervalued assists than your opponents. Everyone wants a first-round talent for a last round price (e.g. Aaron Judge). With teams clamoring to acquire every advantage, they are insistent on wasting away an early draft advantage. In early 2018 drafts I’ve participated in, an early emphasis on middle infielders is inflating their value way beyond their projected production. Is the observation wrong? If so why? If not, how can an owner take advantage of this mispricing?

Note: For this article, I will lump second basemen and shortstops together into one middle infield position. Neither position has more talent than the other and the bottom players will be used to fill a middle infield position.

For those who have recently created mixed-league valuations, positional scarcity doesn’t exist besides with catcher. I use the method outlined in Larry Schechter’s book, Winning Fantasy Baseball to determine my values. I’m not going to go into the process’s exact details but it’s the standard procedure used by fantasy experts to prep for auctions. Even a couple years ago a small amount of positional scarcity existed but a huge influx of good middle infielders has raised the group’s overall talent level up to the other positions.

Read the rest of this entry »

Jonathan Villar burst onto the scene in 2016 with 19 HR and 62 steals. He was going 19th overall in 2017 NFBC drafts and was a huge disappointment when he hit posted a .293 OBP, 11 HR, and 23 SB. With 2018 drafts starting, I’ve seen his ADP (200) way below where he should go using his Steamer projection (~118). The disconnect is understandable but not to the current level. Opportunity exists for huge upside.

Just for reference, here are Villar’s basic stats from the past four seasons and his 2018 Steamer projection.

Of all the values which changed his value from 2016 to 2017 was the 76-percentage point drop in OBP. Less times on base meant fewer steals and runs scored. An OBP under .300 is kill for any hitter.

Besides the scoreboard stats, here are his 2nd order stats over the same time frame.

His plate discipline is the biggest discrepancy over the past two season seasons with his K%-BB% jumping from 14% to 23%. Even though he maintained similar power and groundball rates, the overall decline in contact rate while swinging more did him in.
Read the rest of this entry »

In leagues with Runs and RBIs as categories, predicting how a player’s mix will change with a new team can be guessing. Some clown at Fantrax yesterday wrote the following:

“As for Headley, his value drops by going to a worse offensive team in a pitcher-friendly park. Part of the decline could be offset by a move up in the lineup since he mainly batted seventh for the Yankees last season.”

It could go up, it could go down, who really knows? While writing the statement, I needed a better answer so I created a couple quick and simple tool. If an owner can estimate a few stats, they can predict changes in plate appearances, Runs, RBI when a hitter moves from one team to another.

The key was to be simple and quick. For simplicity, only the following stats are needed.

• New likely lineup location
• Estimate of projected home runs
• Estimated games played such 150 out of 162 games as a percentage.
• Estimated Runs scored by a team. Used over on-base percentage because team level runs scored is easier to find and remember.

The estimated runs scored is the toughest value to come up with. I’d just go to FanGraphs team projection page to get a decent idea. Just take that year’s RS/G and multiply it by 162. Another method is to take the previous season value and plug it into the following regression equation:

`RS in Y2` = .575 * `RS in Y1` + 311

The goal is just to get a basic idea of possible changes.

Read the rest of this entry »