MLB Daily

Umpires can have an effect on MLB games. Each one has his own strike zone thus there will be trends regarding walks and strikeouts. Just like looking into any trends, it is important to weigh it accordingly. I have seen plays solely based on Umpire trends which is ridiculous. No more so than basing a play/wager on any other trend. We do want to ride streaks such as winning and losing streaks, or hot scoring offenses or cold ones, so we must be good at identifying trends and streaks.

Gamblers look to find any edge they can get. It should be no surprise that umpires have gimbling stats! Their stats include Home Team wins and losses, average total, walks, strikeouts, and more depending on where you get the data from. The chart below is a sample form last year.

The premise of ANY trend should be to validate or further solidify reasoning that supports a position in the game. For example, let’s say you like Over 9.5 runs in a game. You find that Cory Blaser is the Umpire. He has an avg. total runs scored 10.36 in his games. This trend is supporting your Over premise. We cannot just take this information and play over based on the total trend for the umpire. Keep in mind, these trends do not show what games he was part of. We do not know if he was in hitter-friendly stadiums or if he was part of some kind of crazy scoring games that skew the data. We can look to see if there is justification for runs being scored due to his strike zone. This is the important part.

A small strike zone means less balls in the strike zone and more balls out of it therefore there should be more walks. The more guys on base creates more scoring opportunities hence there should be a higher total. This works opposite as well. The larger a strike zone is the more strikes thrown thus more strikeouts. This creates less guys on base and lowers the scoring opportunities. These are logical reasonings for the total average which in turn creates legitimacy to repeatable future strike zones, thus consistency in totals markets for this umpire.

A couple of thoughts. One, do not ever make any wager solely based on a trend no matter what that trend is. Two, a trend only has value if there is some reasoning to it which can make it repeatable for a reason. For example, Team A might have won 6 Weds day games in a row. Is that a repeatable/reliable trend? Maybe? Look deeper. Wednesday are get away days. It means teams tend to play early day games so they can fly out to their next city that night and play a game on Thursday night. An origination or manager may be very good at motivating his team in these games. They might be specific “go after” to win type games where the organization has identified to attack these spots to win more games. If you can find reasoning like this, then yes these are repeatable and reliable trends. However, more often than not, you will not be able to support the trend with legitimate analysis. Three, make sure you know if you are betting a streak or a trend. Baseball is a game of streaks over the course of 162 games. We need to ride streaks. Do NOT blindly ride trends!

I have written about velocity changes from year to year and from start to start. We are searching for those pitchers who either gain or lose it. Yesterday I came across a perfect example of how effective this technique is. This data is for John Gant of STL. Several charts are showing below. The first one is SP John Gant velocity changes year over year.

In 2019, he averaged 96.3 mph on his fastball/sinker, and in 2020 he averaged 94.4/93.9. The variance is virtually 2mph. The jump in velocity from his averages from 2016-2018 to 2019 is misleading. He was a starting pitcher in the first part of those years and then a reliever in the latter years. A starter throws more innings thus more pitchers, therefore does not “max effort” every pitch. A reliever throws 20-30 pitches in an outing so max effort on every pitch is normal.

However, he started the spring training game yesterday throwing 79 pitches! He is getting extended or stretched out like a starter. Some interesting data from that start though. Let’s look.

First, let’s notice the velocity since we have been looking there. His average sinker was 90.0 mph. We should be alarmed at how much lower it is from last season (94.4 mph) and how much lower it was compared to his spring so far (93.8mph). The blue coloring indicates the variance in today’s start against his spring averages. We can clearly see he did not have his best stuff as his velocity was down considerably for all of his pitches.

Second, I made another arrow on this chart. It is pointing to a column labeled as CSW%. CSW% is called strikes plus whiffs as a percent to pitches thrown. His total is at the bottom 25. It means of all 79 pitchers thrown 25% of them were called strikes or swings and misses. 25% is not a very high number. We are looking for 36%> and above 40% for a great day. We can see he threw 35 sinkers (44%) of which his CSW was 17%. Again, this means he was not getting called strikes nor swings and misses. This should indicate balls in play and walks, which correlate to runs scored!

As we can see from his box score, he gave up 4 hits, 1 HR, and 2 walks in only 4.2 innings. We need to spot these situations and take advantage of them. At least, be aware there is an issue here and if we can get value against him, we need to look that way. Keep in mind, there are 9 innings to games. STL though trailing 0-2 when Gant came out of the game, ended up winning this game.

As 2021 Spring Training starts winding down, the beginning of the season starts ramping up! April 1 is opening day so there are 2 weeks and 2 days left in camps. These are more crucial times to get answers as to what teams are going to do with their rosters and for us to evaluate / scout pitchers.

MLB teams are going to carry 25 man rosters with a 5 man traveling taxi squad. This is to avoid any flare up problems with Covid. Most teams have started making roster cuts and moves. This means the players are going to make the team and the players who are competing for spots are the ones who remain in camp. Therefore, the quality of competition moves up. The games have more meaning if only for the purpose of the players running out of time to make an impression.

Up to this point, most pitchers have been throwing with the purpose of getting their work in. As we are getting closer to the start of the season, many pitchers will adopt a new philosophy. They need to start trying to get better out rather than just get their pitch count up. The quality of at-bats will improve based on the quality improvement of the batters. The players who were not experienced were mostly sent down. These next couple of starts will more resemble a true game than any we have had so far. It does not mean teams will be going all out to win, but the level of competition will be improving.

We are seeing pitchers at the 50 pitch marks now, and they need to able to go 90-100 by opening day. Most of them will have at least two more starts prior to opening day to stretch out longer. Some of the pitchers got off to slow starts regarding their pitch counts. We need to know who they are so when the season does open, we don’t get surprised by the length of the start.

This means it is time to start monitoring betting lines! We can dabble where we might see edges. Just like any other time, MLB is a game of streaks. Bet on winning streaks and against losing streaks. Spring Training wagering is all about betting value dogs! Please do not wager on favorites. MLB rules now stipulate no rollover innings and teams will play 9 inning games. I have not paid much attention to the lines because I had no interest in betting these games because they were truly exhibitions. I expect that will change some and I will begin looking at wagering odds for the rest of the games starting at the end of this week.

What is regression and Game Score and why should it matter in handicapping? Let’s get some definitions we can work with. First is for regression. I am using the terminology of “regression to the mean”. This is copied directly from a Google search. “In statistics, regression toward (or to) the mean is the phenomenon that if a variable is extreme on its first measurement, it will tend to be closer to the average on its second measurement—and if it is extreme on its second measurement, it will tend to have been closer to the average on its first.” Think in terms of outliers. Going in the direction more extreme than another, the outlier is not the mean (average bar). Humans are not machines and have progression and regression toward the mean. If we could gauge or even predict those “outliers”, then we could bet on those opportunities they present.

                What is Game Score? The next few lines are copied directly from Fangraphs. “Game Score was originally created by Bill James to measure the quality of individual starts.” “Calculating Game Score (original or v2) is extremely simple, requiring some basic addition, subtraction, and multiplication. The original Game Score is calculated like this:

Game Score = 50 + Outs + 2*(IP Completed After the 4th) + Strikeouts – Hits – 4*Earned Runs – 2*Unearned Runs – Walks”

Game Score tries to answer the fundamental question, “how good was that start?

Game Score Rules of Thumb

Game Score	Rating
0-10	Unspeakable
10-20	Awful
20-30	Bad
30-40	Poor
40-50	Below Average
50-60	Above Average
60-70	Good
70-80	Great
80-90	Excellent
90+	Make Sure Your Friends Are Watching

Fundamentally, it is important use Game Score because the Win stat for a pitcher does not indicate the performance of the pitcher nor if the performance was above or below mean average. 

                Most successful gamblers are good at spotting patterns. This is a skill vital to seeing opportunities when others are not even looking. Regression or progression outliers make patterns. We can somewhat predict these patterns with reasonable expectancy. Here is where Game Score comes in. Examples of patterns I am referring to. Starting pitchers earn a GameScore for every game in which they start. As we determine a pitcher’s average GameScore over the course of the season, we will see above and below average GameScore results. We will be estimating future performance based on the patterns created by the outliers in GameScore. Now let’s put this into practice to discover some patterns.

                This chart is of SEA Marco Gonzales (picked random) and is his Game Log. If you are not looing at Game Logs, you are missing important opportunities. We are going to look at the last column GSv2. This is GameScore Version 2. We can see his Total (YTD) was 62 which is above average and for a season is particularly good!

I made some edits on the chart. Circles are well below average outliers, and highlighters are well above average outliers. The log flows bottom to top. We see his 1^st start was a GSc 46. His avg GSc in 2019 was 53 so he is below his normal performance while establishing a new 2020 GSc average. We should expect his GSc46 to be an outlier low, so his next start should either be an average start or an outlier high to get his average back to the mean. In this case, he threw a GSc66. A particularly good outing. His GSc after two games is 56 and above average. This was a good outcome for SEA. Gonzales was on the road against Dylan Bundy, and he was a +162 underdog. SEA won the 8-5. We assumed Gonzales would throw well and he did. We were not sure if he was going to be an outlier high, but his average to the mean left us those two options. His 3^rd start was a GSc58 which was his average which again would be expected. We cannot do much else with Gonzales now until he has another outlier. It came on 08/11 against TEX. He threw a GSc 46 and is an outlier low. We now know he should throw well his next time out. On 08/18 he did just that. He threw his best game season to date a GSc80. You can see how these patterns build and for Gonzales it was quite easy as he threw an outlier high off an outlier low all season.

                    Experience teaches us many things. My experience with this has taught me several nuances. First, this is a great early season betting strategy. I have written the past articles referencing early season betting, and this is the premise of it. Second, when trying to figure out when a pitcher will throw bad or good, understand the quality of the pitcher too. Not all pitchers are the same. Your elite pitchers DO NOT have many if any outlier low games! Example, Shane Bieber GSc72 last season. He did not throw a game under 53! Third, pitchers tend to off set extreme outlier performances more immediately after than gradual. If a pitcher throws a no-hitter for example, he is a great “go against” the next time out. Fourth, pay attention to streaks and compare history. How many consecutive times has this pitcher thrown poorly or well? What is his normal?

                    We can learn so much from details if we are looking at them. Spotting patterns is your edge on the book! Do not miss your chances to exploit them.

What are they and how do we use them?

There are so many stats for baseball and not all of them have value to predicting outcomes. The standard batting average, RBI, and ERA stat for example fit into that category. Stats are valuable when their context has meaning. For handicapping, we need to know who will win the game and how many runs will be scored. A batting average, RBI count, or ERA will not provide us context leading to the outcome of winning the game. This is where the sabermetric stats come into play. As a rule, sabermetric stats are designed to either provide value a player has to team wins, or measured quantity of value to teams wins when compared to other players. The key is they are associated to wins which is what we are trying to determine.

In writing about 3 True Outcomes, I used definitions for FIP and xFIP. Let’s revisit them now. FIP is Fielding Independent Pitching. Its definition: FIP takes a pitcher’s strikeouts, walks, and home runs allowed and translates them into a number scaled to ERA. Think of it as what the pitcher’s ERA should be if the defense behind him turned batted balls into outs at a major-league average rate. Ideally, FIP measures how dominant a pitcher is at limiting baserunners and scoring chances for the opponent. xFIP: is a predictive stat estimating the pitcher’s future FIP based on his current pitching data. It is a great tool for looking at upcoming games. It normalizes a pitcher’s home run rate based on his flyball percentage vs actual home runs hit. This predicts how a pitcher’s FIP will be over his next several starts. SIERA is Skill Interactive Earned Run Average. SIERA: quantifies a pitcher’s performance by trying to eliminate factors the pitcher cannot control by himself. But unlike a stat such as xFIP, SIERA considers balls in play and adjusts for the type of ball in play. Whereas FIP and xFIP are not utilizing balls in play, SIERA does, thus SIERA is the most accurate predictive model between the three. They all appear like the way an ERA looks. SIERA tells us a couple of things. Strikeouts are good, even better than FIP suggests. Walks are bad, but not that bad if you do not allow them to score.

What do they tell us? It is important to know the variation between FIP and xFIP numbers. You should always have them listed side by side or together. The reason is FIP tells the value of a pitcher independent of the defense and xFIP tells you about the ability level of the pitcher and what should be expected. We can see regression and progression here which is what we are looking for. The next charts show two pitchers’ game logs. CLE Shane Bieber (who was the #1 pitcher based on WAR) and SDP Zach Davies (who was the #25 pitcher). I arbitrarily chose #1 and #25 to see what if any the variance in FIP and xFIP was for the two. First is Shane Bieber.

Second, this is Zach Davies.

Using the scales, Bieber was better than excellent! His xFIP is also lower than his FIP. It means his talent and ability is greater than his performance. Zach Davies is interesting. His xFIP is higher than his FIP which means his talent and ability is lower than his performance. He is also showing up in the average area for FIP and below average for xFIP. Then compare his ERA to these results. No matter you slice it up, Davies, although a good season, was not an above average pitcher. This is where we need to investigate SIERA. Remember, FIP and xFIP are largely good for strikeout pitchers and Davies is not that. He has balls in play often so should look at these indicators. Using SIERA, Bieber had a 2.52 and Davies had a 4.32. Again, we see Davies struggle with a below average rating in SIERA.

                The reason I provided a game log for both pitchers is for you see the variation form start to start. We have access to this information! We can see what the pitcher’s predictive stats are prior to the game. HINT: THIS IS EXTREMELY VALUABLE INFORMATION. Look at the games Davies pitched. He threw 12 games. Our predictive stats tell us he should allow 4 runs on average in those games but we know his performance was better than his ability so we should assume 3.5 – 4 runs he will allow on average. He allowed 3 or more runes 7 times out of his 12 starts. We can use this to our advantage! Bieber in the same sense should only allow 2 runs or less. He started 12 games. He allowed 2 or less runs 9 times!

                To express how valuable this knowledge is means on a scale of 1-10, this is a 10! You cannot properly evaluate a baseball game without this knowledge. I use this on a rolling scale. I want to know how a pitcher is throwing today, and a recent history. I use 1 moth of starts which equates to 5-8 starts. I use their FIP, xFIP and SIERA just as described. This tells me an almost complete pitching story with accuracy! I go further into detail with more areas of study, but this is a meat and potatoes part of the equation.

                Hope this was helpful. I am available for questions regarding any of my articles or need clarification of any of the data I use. Ask lots of questions!

MLB has a verbiage all its own. It includes acronyms not many people can remember what they mean, let alone know how to calculate. Plus, it seems new ones are added often. Today, let us discuss 3 True Outcomes. First the definition:

The “three true outcomes” in baseball are said to be a home run, a walk, or a strikeout since none of the three, with the rare exception of an inside-the-park home run or a strikeout with a dropped third strike, involve the defense beyond the pitcher or the catcher.

Why does it get a write up? Well, it is particularly important in understanding MLB today. It correlates with a sabermetric stat called FIP (Fielding Independent Pitching). We need to understand exactly what a pitcher is trying to do to then, understand how a hitter is trying to combat him. The FIP definition is:

FIP takes a pitcher’s strikeouts, walks, and home runs allowed and translates them into a number scaled to ERA. Think of it as what the pitcher’s ERA should be if the defense behind him turned batted balls into outs at a major-league average rate.

FIP measure exactly the 3 True Outcomes. xFIP is a predictive stat estimating the pitcher’s future FIP based on his current pitching data. It is a great tool for looking at upcoming games. HINT! It normalizes a pitcher’s homerun rate based on his flyball percentage vs actual homeruns hit.

Fielding Independent Pitching (FIP)

Rating	FIP
Excellent	3.20
Great	3.50
Above Average	3.80
Average	4.20
Below Average	4.40
Poor	4.70
Awful	5.00

3 True Outcomes are important because of the consistent claims of MLB being boring. In many ways it is. However, you could look at his and gauge how dominant a pitcher is or was which is where we are going with this. Let us say we have the DET Tigers who struck out 27.3% (most in MLB) and BB% 7.1% (which also was worst) and they are facing CLE SP Shane Bieber. Bieber had a FIP of 2.07 and a K/9 of 14.20! We can very clearly see there is a huge mismatch for CLE on the pitching side. Not only is it the worst possible spot for DET, but it could lead to an Under play. Well, on 8/15/2020 this matchup happened. CLE and Bieber were -180 @ DET against DET SP Turnbull. The final score was 3-1 CLE. As the -180 is a line too high we will not likely bet into, the Under 7.5 was a line with value. We would not have or should not have expected DET to provide much offense against Bieber which creates a lean to Under. Once analyzing CLE as the 25^th worst scoring offense, we should have concluded at least to a degree, scoring was not going to come easy.

It is important not only to understand where the better team is but to predict the outcomes of games, we need to know how dominant the pitchers are going to be. The more a pitcher controls and limits base runners, the fewer scoring opportunities the offense has. If both pitchers excel, or like the previous example, one pitcher has dominance and his offense doesn’t score, then we can clearly lean with a totals play. Of course, the data could easily lead us to Over as well too. We need to read what the game is telling us then make logical assumptions of how the flow will go. Game Flow is an important part of handicapping. We need to have a pretty good gauge of how the game will play out. Game Flow is a topic for later times as it is very complicated with details and nuances of the game.

The weather temperature is an important reminder for early season wagering. The colder it is the less the ball travels, thus the less there are damaging hits. The warmer it gets, the more the ball travels, thus there are more damaging hits. Cold weather equals less scoring and warm weather equals more scoring. The charts show damaging hits by temperature and runs scored by temperature. The results are obvious. Starting pitchers have the most effect on these outcomes, but the weather has a significant factor as well. It can be windy too. Wind blowing out has less effect in cold weather but more effect if it is blowing in. Early in the season, make sure to check both the temperature and the wind.

Some pitchers have thrown once, others a couple of times, so we are now getting velocity reports. Although you can find the velocity of just about anyone; I am only highlighting some of the ones with changes. Their velocity can go up or down. If they go up, this is a good sign for the pitcher, and if they go down, then it is a bad sign. These will change more as pitchers ramp up for the season throwing more pitches in a game. I will update the list later to see what changes will carry into the season. If there is more velocity for the listed pitcher, pay close attention. He will be better than expected, thus should be a valuable asset in the opening weeks to our wagering plan. The vice versa is also true, if the pitcher losses velocity, he will be a go against type gut for the opening week and we can get value there too.

So far, BAL Felix Hernandez is in major trouble losing 4.6 mph and well in the danger zone. He is the most velocity lost pitcher. WSH Max Scherzer needs to be watched very carefully. He has lost 3.7 mph. His next few spring starts are worth watching. LAA Shohei Ohtani leads the list in gaining velocity at 4.2 mph. A good sign for him and the Angels. DET Julio Teheran is 2nd on the gaining list. It could be he has found something and can DET some solid innings. He should be watched to see if it continues.

American League Central Preview and Predictions

The MIN Twins won the AL Central in 2020 but it was a nail biter! They were 36-24 while CHW and CLE went 35-25 for the slimmest of a 1 game margin. All 3 teams made the playoffs only to see them all lose in the 1^st round.

The 2021 season should lose CLE from the mis of contenders so MIN and CHW will be competing for the division. There is much hype surrounding the CHW and no one much mentioning MIN at all. CHW had an off-season success with free agent signs and trades. They brought in Adam Eaton (WSH), Lance Lynn (TEX), and Liam Hendricks (OAK). These additions make them better for sure. BaseRuns standings suggest that CHW should have won the Central in 2020 by 2 games over MIN, so MIN is the team in need of catching CHW!

Looking at MIN, we must gaze back into 2019. They led the MLB and set a record with 307 HRs. They were the 3^rd best offense and led the league in ISO. The 2020 team fell all the way to 16^th ranked in offense. They were 6^th in HRs and 11^th ranked in ISO. This is important because we should look at 2020 as a segment of a season and not a season, thus we should conclude MIN will have a terrific rebound with offensive performance. It should start at 3B with Josh Donaldson. He will get a complete season with MIN this year as he is healthy. Their lineup will have all 9 batters with above average wRC+ numbers depending on who is in it. Their starting pitching is where they are not elite but have capable starters. Kenta Maeda “saved” them last year producing his best season ever. We should look for a regression from him. Jose Berrios is an established #1 but is no. ACE. They brought in JA Happ who is serviceable and have quality from Michael Pineda. It is not terrible but for a contender, it is not overwhelming either. Their bullpen is good. It ranked 3^rd in MLB last season and maybe that was a stretch. It projects 12^th in 2021. Alex Colome will now close games and they will mix and match with the others to get to him. The models like MIN to win 89-90 games. Las Vegas win total is 90 games. I like their offense to light it up.

CHW had the AL MVP last season in 1B Jose Abreu. He finished with 166 wRC+ or was 66% better than an average hitter! His season was more of an outlier though and he should not be expected to produce to those levels in 2021 so a regression from him. Their lineup could have up to 6 over average wRC+ batters. Look for Eloy Jimenez to be the main offensive threat. They have only 2 players projecting with double digit OFF WAR numbers (Abreu and Jimenez). They have a complimentary lineup where they can disperse who get the important hit. Other than Abreu, they do not have a proven RBI guy so they will need to keep their young talent progressing forward. Tim Anderson projects lower as one of the few under 100 wRC+ (98) which is the average. If that happens, it is another big drop off as he produced 142 wRC+ in 2020. Lot’s of questions on how good this offense really is. They may have an ACE in Lucas Giolito. He produced great numbers over 12 starts. His FIP in 2019 was 3.43 and 3.169 in 2020 so he is establishing quality. My concern with the rotation is no one projects under 4.00 FIP for 2021. They have arms and they can pitch well but I cannot really see a consistency of solid performance. Keuchel projects with 4.69, Lynn 4.33, Cease 4.87 and so on. They can rely on the most underrated and now with Hendricks, the #2 bullpen in the league. Hendricks improves the #8 ranked bullpen form 2020 immediately. He was the #1 closer in MLB last season. The models like the CHW to win 88 games (1 less than MIN) and Las Vegas set the win total at 91.5.  

The CHW could be better, and MIN IS better. You can see some of the hype in the Las Vegas line of 91.5 wins. CHW and MIN are neck and neck to win this division. I like MIN! CHW must prove they can overcome Abreu and Anderson regressing before I can predict them as the division winners.

Another interesting development in the Central is the fall of CLE and the rise of KC. KC is my favorite OVER win total bet! Las Vegas has 72.5 wins for them. I think they will compete with CLE for the 3^rd spot in the division. CLE win total is 81.5. Tons of value there!   like KC to win 79 games. KC has brought in some veterans and have a developing rotation. CLE has some front office issues lingering, lost Lindor, but can pitch. CLE needs offense. This will be a fun watch and I think if thing fall right for KC, they can be the 3^rd best team n the division!

Did not mention DET but they are developing. They are going to be better with Mgr. Hinch. They also are not the worst team n the AL (TEX, BAL). Their targets are SEA and KC, and CLE. Will have to see how they grow this season and if Michael Fulmer and make a good return.