I’ve been grinding my way through the various inputs I use in addition to projection to make my team-building decisions. It’s time for the final few lists.

For reference, here are the factors I’ve already published.

• Hitters who played through an injury.
• Starter and Hitter Injury Risks
• Pitcher Single Season Projections and pERA Values
• Hitter StatCast and Single Season Projections

Also, I’m not going to take into account two minor league related factors: Voit/Muncy List (2020 list) and bumping up hitting prospect projections. Without a minor league season, the Voit/Muncy list can’t be generated.

As for bumping up hitting prospects, I’m going to pass this year. I know there will be prospect rankings but I’ll not give two-shits about them. Even with some reports from the alternate sites and fall instructs, the information will be spotty at best.
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Yesterday, I reviewed the list of early fly ball + line drive exit velocity (we’ll just call it EV here) surgers compared with 2019 and looked at their performance over the rest of the season to determine how sustainable the gains were. Today, I’ll review the decliners to see if they rebounded toward their 2019 marks. Was their early season EV declines just a small sample slow start or a sign of a seriously disappointing season?

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After only about a week and a half of the season, I decided to peruse the average fly ball + line drive exit velocity (let’s just call it EV in this article) leaderboard and compare the then-current marks to 2019. Even though the sample was tiny, perhaps we could get an early read on some breakout hitters by identifying the early surgers. So let’s review the original list of surgers and find out how these hitters performed the rest of the way. Did their EV remain at those elevated marks, rise even higher, or regress right back down to or toward their 2019 marks?

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And the 2021 draft prep data dumps continue. Today, the injury chances for hitters and starters take center stage. I’ve been trying to wrap my head around injuries since I’ve started writing about baseball. The reason it’s a difficult subject to nail down is that the information is spotty.

Also, if a player checks all the factors to head to the IL (Injured List), he could have that one magical season (i.e Stephen Strasburg in 2019) when he never gets hurt. Adding to the mess is that the league moved from the 15-day IL to the 10-day IL, back to the 15-day IL (for only pitchers), and then just started making up rules for 2020. Through it all, the following risk assessments cut through a lot of the narratives and are solid going into next season.

Starting Pitchers

During the extended offseason, started reinvestigating my analysis of pitcher injury chances with a literature review, injury chances, and how injuries lead to increase aging. While age and fastball velocity are still factors, career IL days and arm injuries are most likely to point to future injuries and degraded performance. The simple thresholds were 120 career IL days and three or more arm injuries. Here is a list of the starters (min 10 IPin 2020) who meet both criteria (full list).
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Introduction

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 1^st round selection, a 2^nd round selection, a 3^rd 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 3^rd round player in the 3^rd round, or a 9^th round player in the 9^th round, etc. The key is to draft a 3^rd round player in the 10^th round, and a 9^th round player in the 20^th 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.

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I just released my two simple projections and Gary Sánchez could not be any more polarizing. Using just last season’s stats, he projects to post a replacement-level .656 OPS. And when I use multiple years of StatCast data, he projects for a .889 OPS, the 4th best overall value. The difference comes down to BABIP regression.

To say Sánchez had a disappointing 2020 is a huge understatement. For the hitters with 170 or more plate appearances, his .618 OPS ranked 13th worse. His .147 AVG was the worst. It was fueled by a combination .159 BABIP (2nd worst only to Edwin Encarnacion) and 36% K% (6th worst). The single-season projection’s regression raises his BABIP up to .223 and his AVG to .193. They are better but still far from rosterable.
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Last season, I introduced two independent projections to help fantasy managers evaluate players. One is based on just the hitter’s previous season production and the other is based entirely on StatCast data. It’s time to have them available for 2021 draft prep.

As I previously stated, this is the reason behind the projections:

I created the projections with inspiration from “The Model Thinker” by Scott Page.* The author states, “do not put too much faith in one model”. To further explain this stance, he states:

“The lesson should be clear: if we can construct multiple diverse, accurate models, then we can make very accurate predictions and valuations and choose good actions.
…
Keep in mind, these second and third models need not be better than the first model. They could be worse. If they are a little less accurate, but categorically (in the literal sense) different, they should be added to the mix. “

Several projection systems already exist. Other projections take many projections and combine them. The issue is that projections are exclusively based on the previous season’s results (e.g. stolen bases, home runs) while incorporating some various levels of regression, aging factors, and yearly weightings. My goal is to create projections that don’t follow this standard cookie-cutter formula. I expect the projections to not be the most accurate because “all models are wrong.” I’m wanting a unique perspective on a hitter’s talent.
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Every year I take the bold and daring adventure known as bold league leaders. I rarely get any of these right, but it’s fun to decide on the names, and might give you the little nudge you need to roster a chosen player. In a shortened season, it’s a little easier to get one of these right since more luck is involved, which I need a lot of if I’m not picking the obvious choices. So let’s see how I did with my 2020 Bold Hitter League Leaders.

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The 2021 draft season may be the most unpredictable one … ever. While several rule changes (e.g. NL DH) can cause some evaluation inconsistencies, the biggest monkey wrench to deal with will be the lack of previous season stats. Projection systems will weight the 60-game 2020 season (37% of a normal season) to historical averages but what are those who don’t use traditional projections systems? Or read or listen to people who don’t care for them in order to create a compelling narrative? It’s time to anchor some historical context around those narratives.

All analyses “should” start with at least some set of weights. For those who start with one, they’ll have an advantage. For those who guess the correct one, they’ll have a huge edge. The issue with setting weights is how to create a short season one that fills all the narratives.

First, most of the players were rushed to get game-ready and were not 100% ready when the season started. So the beginning of the season stats could be used to replicate this ramping up period, but the weather was still warm at the season’s end increasing offensive output. Maybe the entire first month should be ignored with everything in flux. Possibly the last two because that were the 2020 regular season months. played. So, I decided to look into all the possible options including a horrible first attempt.
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Over a shortened 2020 season, I suggested it might be a good idea to discount ratios, such as batting average, and buy more stable counting stats, like home runs. As such, I identified and discussed seven cheap power buys worthy of buying. The thinking was that you would get your homers, while the projected low batting averages reducing their costs would be volatile and might not necessarily finish at a negative value. Let’s see how the players ended up doing.

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