Our desire to reach into the future will always exceed our grasp. But debunkers go too far when they dismiss all forecasting as a fool’s errand.

Philip E. Tetlock, Superforecasting

Whether you are ranking players straight up or in tiers, projecting what their stats will look like after game 162 of the upcoming MLB season, or using someone else’s projections then tweaking based on your opinion, you are forecasting the future. A necessary skill in fantasy sports.

When we acquire a player in fantasy baseball drafts, we are essentially buying stock in that player hoping to make a profit. The player needs to produce the stats necessary to help defeat the other teams in the league.

I’ve been playing fantasy sports for a long time and I’ve always been a, “use somebody else’s projections to calculate player values but don’t follow them religiously,” kind of guy. I believe I am not alone in this regard.

Over the years I’ve always been fascinated, not that other people sit down creating baseball projections, but that they make them available for others to scrutinize.

Why Am I Creating Baseball Projections?

Creating baseball projections are my blue duck. I’ve always wanted to do my own projections for fantasy baseball but was too intimidated until I read Mike Podhorzer’s (@MikePodhorzer) Projecting X 2.0: How to Forecast Baseball Player Performance. He does a tremendous job of explaining and demonstrating how he does it. Here’s an example:

The most precise way to go about developing a player projection is to forecast the individual components that affect the statistic in question. For example, instead of manually projecting a hitter’s home run total, the more effective method is to project the variables that directly influence it – the hitter’s strikeout and fly ball percentages and home run per fly ball rate.

In the book, Mike guides you through setting up a spreadsheet for your projections. Tanner Bell (@smartfantasybb) took it a step further over on SmartFantasyBaseball with his own spreadsheet (same link as above). With Mike and Tanner providing the foundation, I decided I was going to make my own projections.

I’m doing something I’ve always wanted to do… my own projections!

Inspired by @smartfantasybb and @MikePodhorzer.

For some reason I chose the Orioles as my first team to forecast.

Yikes. Things can only get better from here, right?#fantasybaseball

— Roto Chris (@Roto_Chris) January 9, 2020

Note the date of that tweet. This took a loooong time.

What I Learned Creating Baseball Projections

Time Consuming

When I started I had every intention of analyzing each player and… I don’t have the time for that. I have a real 40+ hours a week job. Plus, I’m not nearly knowledgeable enough to make hunches if a player will or will not improve this rate or that rate.

So I decided to build a formula that provided me with what I need for the spreadsheet. This formula takes into account the player’s past MLB and AAA history. It also takes MLB averages into account because, you know, regression and all that.

Now I’m not going to offer up the exact formula because I’m afraid I would react the same way Moneyball’s Billy Beane reacted when he gave his pie to Monica Gellar.

Chasing Perfection

I learned to chase perfection. Early on in this endeavor, I started calculating the Pythagorean W-L records of each team based on runs scored by hitters and runs allowed by pitchers. My thinking was that if the W-L records passed the eye test then I was probably on the right path. Uh oh.

I purposely started with a, “worst to first” mentality. So, I quickly realized I had a problem when Baltimore’s Pythagorean W-L was 81-81, Detroit’s 79-83 and Miami’s 84-78. Something was not right. Digging deeper proved that the issue was with pitcher’s projected earned runs. My projected league-wide ERA was 4.01, something that has only happened in the “real” MLB five times since 1995.

So I went back and double-checked Mike’s formula for calculating earned runs. The book mentions that the formula he used is a modified version of the Base Runs formula. On that same link is another version of the formula. I plugged that one in and the results were much more palatable. League-wide projected 4.61 ERA which is a tad high but closer to last season’s 4.51 MLB ERA.

While I learned to chase perfection, I also learned that I will never catch perfection. There is no way I will ever be able to get league-wide projected runs scored to equal runs allowed. Therefore, the Pythagorean W-L records will never be perfect. I spent way too much time trying to accomplish this. Way too much.

Playing Time

Playing time is another aspect that I dwelled on too much. I started with Steamer’s projected playing time and then tweaked to get down to somewhere between 6,000-6,200 for each team. I repeated the process but factored in ACT projections. Ultimately I ended up comparing projected playing time from various sources to get to my final number.

Conclusion

Overall, I’m glad I finished my projections. Maybe I should have opened with that.

I thought about throwing in the towel a couple of times but stuck to it. I am already thinking of ways to improve the process for next season.

This is my weekly column where I suggest streaming low owned starting pitchers that have a match up in their favor in the coming week, in an effort to help your fantasy baseball team. This week I’m going to recap all of my suggestions from the recently concluded 2019 MLB regular season.

As a reminder, my process is to look at wOBA, ISO and K% because they tend to identify weak lineups and conversely for pitchers I look at wOBA, ISO against and their K%.

Before we look back at the 2019 season, let’s look back at my previous recommendations for starting pitchers to stream back in Week 26 and Week 27.

Evaluating Streaming Starting Pitchers Suggested in Weeks 26 & 27

Lamet’s scheduled start on Sep 29th never happened. Of the starts that did happen, it is safe to say that I am happy with three out of the five.

Here’s a look back at the two-start pitcher I recommended back in Weeks 26 and 27. Funny thing happened though. I suggested Kyle Gibson for two starts in Week 26 and neither materialized. His first start was later in the week against a different opponent and, as a result, his second start was pushed into the following week.

The Royals opted to not start Bailey after the game against the Angels.

2019 Postmortem

Here’s a monthly breakdown of all the pitchers I recommended this season.

I take solace in the fact that my overall 4.44 ERA was less than the 4.51 MLB average ERA. I do not take solace in the fact that it is a crappy ERA to have on your fantasy team. For example, the team with the worst ERA had 4.387 in my TGFBI league.

Here are some pitchers that ended 2019 with a higher ERA and WHIP than I did:

• Jon Lester – 4.46 ERA and 1.50 WHIP
• Jose Quintana – 4.68 ERA and 1.39 WHIP
• Ivan Nova – 4.72 ERA and 1.45 WHIP
• Martin Perez – 5.12 ERA and 1.52 WHIP
• Rick Porcello – 5.52 ERA and 1.39 WHIP

The point I am trying to make is that I, too, had a disappointing year.

Most Frequent Opponent

The team I picked on the most was the San Francisco Giants. In the 10 games I told you to stream against them I posted a 4.96 ERA, 1.46 WHIP, 55 strikeouts and a 3-5 record. Awful.

The team I had the most success picking on was the Chicago White Sox. Unfortunately, I only suggested streaming against them 6 times for a 2.90 ERA, 1.23 WHIP, 29 strikeouts with a 5-0 record.

Most Frequent Pitchers Recommended

Here are the pitchers I suggested streaming more than 3 times this season:

• Anibal Sanchez: 3.58 ERA, 1.23 WHIP, 3 wins and 17 strikeouts in 5 starts.
• Dinelson Lamet: 1.96 ERA, 1.04 WHIP, 1 win and 33 strikeouts in 4 starts.
• Tommy Milone: 3.86 ERA, 1.00 WHIP, 3 wins and 20 strikeouts in 4 starts.
• Spencer Turnbull: 6.55 ERA, 1.73 WHIP, 0 wins and 20 strikeouts in 4 starts.

All four combined: 3.94 ERA, 1.25 WHIP, 7 wins and 90 strikeouts in 17 starts. That’s roughly 17 Zach Wheeler starts.

My Best Calls

1. Dinelson Lamet (8/6 @ SEA): 7.0 IP, 0 ER, 2 H, 4 BB, 12 K and 1 win.
2. Mike Soroka (5/4 @ MIA): 7.0 IP, 0 ER, 3 H, 2 BB, 6 K and 1 win.
3. Wade Miley (6/9 vs BAL): 6.0 IP, 0 ER, 6 H, 2 BB, 5 K and 1 win.
4. Jake Odorizzi (5/10) vs DET): 7.0 IP, 0 ER, 1 H, 0 BB, 5 K and 1 win.
5. Jeff Samardzija (7/7 vs STL): 7.0 IP, 0 ER, 4 H, 0 BB, 2 K and 1 win.

My Worst Calls

1. Merrill Kelly (7/23 vs BAL): 2.1 IP, 7 ER, 8 H, 0 BB, 4 K and 1 loss.
2. Griffin Canning (7/25 vs BAL): 2.0 IP, 5 ER, 5 H, 1 BB, 2 K and 1 loss.
3. Framber Valdez (6/26 vs PIT): 3.0 IP, 6 ER, 8 H, 1 BB, 2 K and 1 loss.
4. Brendan McKay (8/19 vs SEA): 2.0 IP, 3 ER, 3 H, 3 BB, 1 K and 1 loss.
5. Erik Swanson (4/28 vs TEX): 4.0 IP, 6 ER, 11 H, 0 BB, 2 K and 1 loss.

How Can I Get Better?

There is no other way to say this. I took a big step back in 2019 from 2018, where I put up a 3.64 ERA, 1.20 WHIP, 388 strikeouts and 31 wins in 71 starts.

What happened this season?

I suggested more pitchers to stream (89 in 2019 compared to 71 in 2018). That’s more Russian roulette trigger pulls. So, maybe in 2020, less reaching each week for suggestions?

Another factor to consider is home starts versus road starts. Home/road splits are considered in my analysis but numbers don’t lie:

I just might have to seriously consider only suggesting road starts if it is an overwhelming advantage for the pitcher. Something to think about in the off season.

Well, that closes the book on Straight Cheese for 2019. I hope you enjoyed my analysis and I look forward to improving on my suggestions in 2020!