Often, when an analyst reviews his or her draft, it is typically ahead of the season about to be played. That makes sense! We are planning for 2021, not 2020. It might behoove us, however, to review how drafts actually went. I’m guilty of doing the former and not the latter! With the dawn of the second annual Razzball RazzSlam best ball tournament upon us, I figured now is as good a time as any to rectify this.
Last year, I entered the inaugural RazzSlam having played, and subsequently bombed, in a couple of best ball drafts on Fantrax. I wanted to dip my toe in, get my feet wet, and other joint-aquatic/podiatric idioms, so I entered a couple of low-stakes leagues, figuring I could wing it. As foreshadowed, I fared poorly. I, in fact, could not wing it.
Having failed somewhat spectacularly for a guy who fancies himself at least somewhat knowledgeable and/or adept at fantasy baseball, I wanted to right my wrong by taking my preparation for RazzSlam seriously. I finished 15th out of 216 participants (18!leagues of 12 teams each), somehow not winning my league but ending up one of the highest 2nd-place finishers above some league winners. I’m proud! Because I expected another unmitigated disaster.
Hence, I figure it might be worth reviewing, with hindsight, one of my rare successful drafts. We played out the season already, so instead of trying to outline tips and tricks up front, it might be easiest to simply show you the draft results, each player’s stats, and the clear takeaways from my strategy — some intentional, some not.
Read the rest of this entry »
In 2019, Brad Johnson and I published a weekly series in which we, each on a semiweekly basis, identified three or four or five players in the Minor Leagues who (1) had not appeared on previous top-prospect lists and (2) appeared to us to be capable of producing admirably, perhaps significantly, at the big-league level at some point for fantasy purposes.
Because of an actual force majeure (i.e., the COVID-19 pandemic), Peripheral Prospects was rendered temporarily null as the Minor League Baseball season was cancelled. Alas, we published nothing about peripheral prospects. But that does not mean peripheral prospects did not thrive! Peripheral prospects indeed thrived.
I figured it would behoove me to not only review my favorite peripheral prospects from the end of 2019 but also highlight my favorite (existing) peripheral prospects heading into 2021, before a whole new batch of peripheral prospects is anointed. Yesterday, I revisited my 10 favorites from 2019; today, I’ll highlight another 10 eight whose progress I’m eager to monitor in 2021.
Presented in chronological order (and not by favoritism):
I figured it would behoove me to not only review my favorite peripheral prospects from the end of 2019 but also highlight my favorite (existing) peripheral prospects heading into 2021, before a whole new batch of peripheral prospects is anointed. Here, I’ll revisit my 10 favorites from 2019; next time, I’ll highlight another 10 whose progress I’m eager to monitor in 2021.
Many thanks to Derek Carty of RotoGrinders for his assistance on this article, and for his player notes on a few 2020 player projections.
In my previous article, 2020 Projection Systems Comparison – A Game Theory Approach, I compared several excellent projection systems in terms of fantasy baseball profitability for 2020. It was not the typical statistical comparison, rather – I used a game theory approach. This was the third such annual article that I had put forth in evaluating projection systems.
Earlier this year, Derek Carty unveiled a new version of his already excellent THE BAT projection system. The new system is called, THE BAT X. The major innovation of THE BAT X is that it incorporates Statcast data into the fold. You can read more about THE BAT X works in Carty’s introductory article found here on the pages of FanGraphs.
I have typically evaluated THE BAT within my 2020 Projections comparison. With this season (despite the short duration) as the inaugural run of THE BAT X – Derek asked me to take a deeper look into how his new projection system had performed. To do this, I went back and revisited the same game theory methodology applied to THE BAT X. The initial results look very promising for the young system.
In this article, I will go through what had changed between THE BAT and THE BAT X as far as the game theory simulations. For a few of the largest and most impactful player performance differences, I will also include some analysis from Derek Carty himself as to why THE BAT X made those adjustments. Read the rest of this entry »
Back in 2018, I introduced a game theory approach for comparing baseball projection systems. Proudly, the article was nominated for Baseball Article of the Year by the Fantasy Sports Writers Association (FSWA). The game theory methodology is now back for its third straight year.
This approach is not the standard projections comparison analysis that most others embark on. The typical comparison makes use of some type of statistical measure. The standard analysis involves calculating least square errors, performing chi-squared tests, or perhaps even hypothesis testing. My method does not use any of these capable methods.
On June 23, Commissioner of Baseball Robert D. Manfred, Jr. announced that Major League Baseball would begin its 2020 regular season on July 23rd. It submitted a 60-game regular season schedule for review by the MLB Players Association. The proposed schedule featured divisional play, with the remaining games being played against their opposite league’s corresponding geographical division.
That 60-game proposal came to fruition. It was an unusual season to say the least. The St. Louis Cardinals did not play a game from July 30th through August 14th. Doubleheader games were all seven innings each, and extra innings started with a runner on 2nd base. The designated hitter was in effect for the National League, and so on, and so forth. In the end, the season came and went, and the Los Angeles Dodgers were crowned as champions of the Fall Classic.
Now we are squarely in the midst of the baseball offseason. Most fantasy baseball players are on holiday from their annual game, eagerly awaiting one of the most important ingredients to their annual draft preparation …
Projections.
For many (including myself), player projections are the backbone that form the strategies and planning for the upcoming fantasy baseball season. Understanding how player statistics are forecasted for the coming season is the essential part of fantasy preparation.
Previously, I looked at the largest auction player bargains of 2020. These were the players who were highly profitable after considering their opportunity cost of acquisition. Value should always be considered relative to cost.
We defined the bargain amount as:
$Bargain = $Value – $AAV
We defined $Value as the accumulated 5×5 full season rotisserie value of each player, and $AAV as the average auction cost to purchase the player pre-season. We made use of the July NFBC Average Auction Values, which was one of the best sources of “market” data this year.
Whereas I previously looked at the players who generated the most excess value in 2020, today’s attention will be directed to what I refer to as the value drainers. These are the largest “rip-offs” of the season – i.e., the players who earned the most negative profits for fantasy owners on a full season basis (net of their auction price).
Prior to unveiling 2020’s most unprofitable players, it is important to discuss one additional step in the analysis – the capping of values. I have previously spoken about this concept, but I will touch on it again today.
Eduardo Rodriguez was a player that I drafted on a few of my fantasy rosters this season. His NFBC average auction value during July drafts (auctions) was $7. In Tout Wars, I acquired the Boston pitcher for $10. Unfortunately, Rodriguez came down with COVID-19. He developed heart complications due to the virus, and consequently did not pitch a single inning in 2020.
The question is – what value did Rodriguez accumulate in 2020? What damage did he cost to your team’s aggregate value? Owners certainly lost their original investment on him, but how much more were they penalized? He wouldn’t have made it to one’s active roster – but how much did it cost owners for Eduardo taking up a bench spot?
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)
OR
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.
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 Total Pitches
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 »