The key to succeeding in fantasy baseball:
Maximize the value of your accumulated roster.
At the start of a draft, each fantasy owner is handed a set of draft picks. Each owner receives a 1st round selection, a 2nd round selection, a 3rd round selection, and so on. If your league chooses to hold an auction rather than a more traditional serpentine draft – each team is handed $260 at the auction start. Players are then purchased throughout the auction with the use of these finite funds.
The key to gainfully drafting is not to draft a 3rd round player in the 3rd round, or a 9th round player in the 9th round, etc. The key is to draft a 3rd round player in the 10th round, and a 9th round player in the 20th round.
In an auction, if you purchase every player at his projected value, you will have paid $260 of auction dollars for $260 of value. What you will have is an average team. You won’t finish last, but you won’t finish first. Instead, with your $260 – you need to buy some $290 or $300 or $310+ of total value.
The key is to make a “profit” on as many roster spots as you can. The goal is to purchase players at bargain prices.
I have asked this question before – but it is worth asking every now and again. Suppose that you competed in an NFBC fantasy baseball auction back in July this season.
Which player was the better purchase?
Bryce Harper (OF, PHI)
Andrew McCutchen (OF, PHI)
Before opining on the better Philly outfield purchase of 2020, let’s take a look at their final 2020 stat lines:
On the surface, it seems like a pretty obvious answer. Harper had more HR, SB, R and a better batting average than McCutchen. He had just one fewer RBI.
Read the rest of this entry »
The 2020 MLB regular season has now concluded. In most years, this introductory sentence would be a simple fact. One ordinarily would not pay much attention to such an evident truth. However, in 2020, the consequence of baseball completing the year without a major full stop is a sparkling achievement.
Yes, the Marlins and Cardinals did not play for the course of about a week due to team COVID infections. Yes, there were more make-up doubleheaders played in 2020 than in any season during my lifetime. Yes, there were a few teams that made the playoffs despite a losing record. Yes, the league-wide batting average of .245 was the 6th lowest full-season mark since 1900.
But baseball made it through, and now embarks on their expanded playoffs journey.
As such, it is now time to check back on how we fared in the fantasy season. For me personally, it was a rather positive one. I did not finish below 6th place in any league that I played in this year. Amazingly, I was crowned as the 2020 Tout Wars Head to Head League Champion, my very first expert league title. 2020 showed that the ATC projections work well, even in smaller sample sizes.
In today’s article, I will recap my 2020 bold predictions. To remind the reader, the goal at the outset was to predict 70th to 90th percentile events (10% to 30% likely occurrences). I don’t expect to get the majority of these correct. If I wanted to achieve a higher success rate, I would simply have predicted that Jacob deGrom would win the Cy Young award, and the like.
Now let’s recap! Read the rest of this entry »
This is the fourth article in my wPDI vs. CSW series. You can catch up by reading the first three articles – on called strikes, whiffs and residuals.
Here is a quick summary of some of the basics of wPDI & CSW from this series:
Last year, I developed the Weighted Plate Discipline Index (wPDI) framework, whereby all pitches can be classified into six different outcomes as follows:
Each outcome is then assigned a weight, or an index. A% through F% are the percent of pitches thrown in each outcome. The general formula for wPDI, the Weighted Plate Discipline Index is given as:
wPDI = IndexA * A% + IndexB * B% + IndexC * C% + IndexD * D% + IndexE * E% + IndexF * F%
wPDI can generate an all-in-one sortable metric used to evaluate pitchers. The plate discipline framework may be tailored to mimic (or to correlate to) various measures of deception or effectiveness.
In the first three articles of this series, we developed indices for wPDI to approximate the PitcherList metric, CSW. The Called Strikes + Whiffs (CSW) statistic was featured in last year’s FSWA Research Article of the Year by Alex Fast, and is defined as:
Called Strikes + Whiffs
We separately tacked the called strikes and whiffs components, and landed on the following wPDI equation to represent CSW: Read the rest of this entry »
This is the third article in my series – wPDI & CSW. You can catch up by reading the first two articles – on called strikes and whiffs – found here and here.
Here is a quick recap of what we have covered so far:
In this series, we are looking at the PitcherList metric, CSW and how it relates to my plate discipline framework, wPDI. Last year’s FSWA Research Article of the Year by Alex Fast featured CSW, which is defined as:
With the Weighted Plate Discipline Index (wPDI) framework, all pitches are classified into six different outcomes as follows:
This is the second article of my series – wPDI vs. CSW. For those new to either metric, I will quickly catch you up. [The opening article can be found here.]
In last year’s FSWA Research Article of the Year, CSW Rate: An Intro to an Important New Metric, Alex Fast of PitcherList examines his site’s pitching statistic, CSW. The short and simple formula for CSW is defined as follows:
Independently, I came up with the concept of Weighted Plate Discipline Index (wPDI). With wPDI, we ask just three questions, or three binary events for every pitch:
Every pitch can then be classified into 6 possible pitching outcomes based on the above. The definition of each outcome is as follows:
Each outcome is then assigned a weight, or an index. The formula for wPDI, the Weighted Plate Discipline Index is then given as:
A% through F% are the percent of pitches thrown in each outcome, and the indexes are linear multipliers to obtain the aggregated, sortable metric.
What CSW has most in common with wPDI, is that it shares the same denominator – Total Pitches. That being the case, we can attempt to use the wPDI framework to express the PitcherList metric. CSW is rooted in Baseball Savant data, while wPDI is fed by FanGraphs figures. By exploring the similarities and differences between the metrics, we can also uncover some great nuggets of understanding.
Last year’s FSWA Research Article of the Year, CSW Rate: An Intro to an Important New Metric, was awarded to Alex Fast of PitcherList. In his article, Alex presents the pitching statistic, CSW – a metric which was originally coined and created by Nick Pollack in 2018. As cited in the author’s article summary, CSW is more predictive than Swinging Strike Rate (SwStr%), and is more descriptive than Whiff Rate (Whiff%).
The short and simple formula for CSW is defined as follows:
I enjoy elegant formulae. Sure – wOBA, wRC+ and the like are extraordinary metrics in their own right, but they are not the simplest to jot down. CSW is plain, simple, easy to understand, and nicely predictive.
Coincidentally, and unknowing of CSW, I came up with the concept of wPDI back in 2018. I then published my first works of the plate discipline framework on April 2, 2019. The original article was entitled Introducing: Weighted Plate Discipline Index (wPDI) for Pitchers, and can be found here.
What jumped out to me immediately upon reading Fasts’s article – was that the two metrics have something very in common. CSW and wPDI both share the very same denominator – Total Pitches. The base of both of our metrics are identical. Both utilize the very same sample size, both stabilize just as quickly, and both describe baseball through the very same lens – the pitch.
As a quick reminder of how wPDI works, every pitch can be classified into 6 possible pitching outcomes.
Opening day is finally here!
In 2020, we had two distinct draft seasons – both in February/March as well as in June/July. Some fantasy teams of mine were drafted four or five months ago, while others were assembled just this past weekend. We typically spend all winter longing for the time when our fantasy teams finally start accumulating statistics. This year, due to the tragic global pandemic of COVID-19, we had to wait even longer. We are now finally here. Tonight the standings go live!
I am well aware that there is still much suffering in the U.S. and in the rest of the world from the disease. I do not mean to make light of the world’s situation by any means in my enthusiasm for baseball’s return. At the same time, watching our nation’s pastime played day in and day out, may aid the morale of the country. Although there will be many challenges, I am hopeful that the MLB will be able to start and finish the abbreviated 2020 season without major hiccups.
In my previous article, I gave an update on my Weighted Plate Discipline Index (wPDI) metric. wPDI arises from the core ingredients of plate discipline – looking only at zone rates, swing rates and contact rates.
An important distinction regarding wPDI, is that its sample size is quite a bit larger than other statistics. Many other stats are based on innings pitched, or even per plate appearance. The denominator of wPDI is pitches. While batter outcomes such as strikeouts and walks stabilize fairly quickly, wPDI can work even faster.
Let’s now take a look at the 2019 leaderboards for wPDI, to see if we can find some undervalued players.
Above is the 2019 wPDI leaderboard for starting pitchers.
Blake Snell lead all starting pitchers in wPDI in 2019. The key to Snell’s success was his “out of the zone” plate discipline. In particular, Snell’s Outcome A (out of the zone, swung on and missed) was the 2nd highest of all qualified pitchers in baseball. In 2019, Blake produced a K% rate of 33.3%, the highest of his career. He logged a whopping 147 strikeouts in just 107 innings pitched. Both FIP and xFIP (3.32 & 3.31 respectively) agree that his 4.29 ERA last year was somewhat unlucky.
Last year, I introduced a new (yet simple) pitcher metric. Weighted Plate Discipline Index (wPDI) arises from the core ingredients of plate discipline from the point of view of the pitcher – control, deception, and contact.
wPDI looks at the following basic binary events:
Weighted Plate Discipline Index (wPDI) does not look at generated bat speed, exit velocity, pitch speed, or quality of contact, etc. wPDI doesn’t even focus on walk rates or strikeout rates, or any other plate appearance result. wPDI focuses solely on the pure components of a pitch. Is the pitch in the zone? Is the batter swinging at pitches in the zone? Is the batter swinging at pitches outside of the zone? Is the hitter contacting the pitch?
In this series of articles, I will be refining and expanding upon what I had started last year. I will look at wPDI’s effectiveness and predictability. Along the way, I shall highlight both pitchers and hitters who catch our eye based on great (and poor) plate discipline performance.
The following is the second part of my 2020 LABR Mixed Auction recap. You can read Part I of my recap here. This was the inaugural season of the new LABR Mixed Auction league, and my very first expert auction league.
In my Tout Wars recap series, I talked about how to adjust projections for a particular league format, the proper hitter/pitcher splits to use, and how to create a market pricing curve. I also discussed at length about how to scout your opponents, and to use it to your advantage.
In Part I of my LABR recap, I talked about how to create an initial plan, and how to set an auction budget.
Today’s article will focus on a topic that is barely discussed in the fantasy community. However, I believe it to be a large key in managing your auctions, and crucial in the quest to accumulate the most fantasy value at the draft table. I am referring to player nominations.
If fantasy baseball drafts are akin to a game of checkers, auctions are in many ways a multi-player game of chess.