I will show you today, how you can analyse baseball games and we will take a game between Boston Red Sox and Toronto Blue Jays as an example.

Boston Red Sox and Toronto Blue Jays will play the first game of a 3 game series in Canada and both teams had a day off yesterday. Boston Red Sox will start with Rick Porcello, right handed pitcher, who is 4-0 to the season with ERA of 1.40, whip 0.818 and 23/1 K/BB rate in 25 innings. Toronto Blue Jays on the other side will start with J.A. Happ, let handed pitcher, who is 3-1 to the season with ERA of 4.50, whip 1.273 and 31/7 K/BB rate in 22 innings so fart this season.

Boston is 17-4 to the season and they have an amazing start so far. They score 5.9 runs per game and two of four losses came in last two games. They lost two straight games in Oakland.

Toronto Blue Jays on the other side is 13-8 to the season and they score 5.5 runs per game. They are coming home after two big losses against Yankees on the road. Yankees won 3 out of 4 games in this series and in the last two games, they outscored Blue Jays by 14-2 in runs.

Those are just basic information that we can find anywhere and of course, this is not enough to make a final bet.

So, let’s start with couple of different approaches. Sports handicappers make bets on a different ways. Some bettors will make bets just based on their gut feeling, some bettors will just analyse line movement, some bettors will use strictly statics bets and some will combine all together. We have different methods, we have different styles and if your method is making a profit for you, this is ok.

I use my betting model to estimate my winning percentages. I turn those winning percentages into the odds and then I compare my odds with the bookmakers odds. My betting models are the heart of my sports betting decisions. But before we go to my projected odds on this game, let’s start with the odds. The odds are the key. Betting is not about finding the winners, but about finding the value. In other words it is about finding the right price in a sports betting market. And if you can get better price than market, then you are doing well. But to know what is a good price we need to research games and estimate our own odds somehow.

So, let’s research this game game as a sports bettors, not as a sports fans.

OPENING ODDS AND MOVEMENT

The first thing you can do is to check the opening lines or the odds if you like. There are couple of free sites, that share this information. Oddsportal and SBR are two of them.

I will take a look at Pinnacle sports odds. Pinnacle is probably the sharpest bookmaker in the world and what kind of information you get if you check the lines?

First (BOOKMAKERS OPINION) – when Pinnacle open the lines, you get their first opinion on this game. Of course this is not final, because they will later move the line because of betting action and possibly also because of some other information that they will get later (injuries, lineups,…). But let’s check their opening odds:

TORONTO – 2.11 (+111)

BOSTON – 1.83 (-120)

We can say, that this is bookmakers opinion on this game. We can easily turn those odds into percentages to see how much chances they give to home and away team. If we divide 1/2.11 and 1/1.83 we get implied probabilities. They added some margins this is why when you sum both numbers you will not get 1, but little bit bigger number:

1.83 0.5464480874
2.11 0.4739336493
SUM 1.020381737

If we cheat little bit, we can get probabilities on this game:

ODDS Prob. (Margins) Probability
1.83 0.5464480874 0.5362572191
2.11 0.4739336493 0.4637427809
SUM 1.020381737 1

 

I simply take out the margins (juice): (1.020381737 – 1)/2

So, what kind of information you get with opening odds?

Bookmakers gave 53.63% of chance to Boston and 46.37% of chance to Toronto. In other words if they repeat this game a lot of times, bookmakers think, that Boston will win around 54 times out of 100 and Toronto 46 times out of 100.

Second (Bettors opinion) – After the opening odds we usually see some line movement and usually professional bettors opinion is little bit different then bookmakers opinion:

04/23 11:10 PM 1.93 2.00
04/23 09:38 PM 1.88 2.05
04/23 09:37 PM 1.91 2.02
04/23 09:26 PM 1.89 2.04
04/23 09:25 PM 1.88 2.05
04/23 09:24 PM 1.86 2.07
04/23 09:23 PM 1.87 2.06
04/23 02:41 PM 1.85 2.08
04/23 01:56 PM 1.83 2.11

The initial line movement shows, that there was an action on Toronto and bettors tried to get better price here. The odds started dropping later too. But initial line movement is something that we like to see. Note also, that sometimes such movement can be done intentionally too, because of better price later. But in general, initial line movement show that there is some action on Toronto. Bookmakers will move the line because of sharp bettors action. And we as a bettors we like to be on sharp side, not on public side.

Third (public opinion) – After bookmakers open the lines and move the line, public will later jump and there will probably be some other movement couple of hours before game starts.

What I wanted to show you here is that you can always check what is bookmakers opinion on games in terms of probability and what are initial line movements. If you estimate your chances and if bookmakers have the chance of around 55% on one team and you think or estimate your own odds on this team at around 95%, then something is wrong. Bookmakers opinions are pretty good and we are looking for games, where we think that their probabilities should be little bit lower or little bit bigger.

So, bookmakers gave Boston better chance, but the odds dropped after one hour on Toronto from 2.11 to 2.08 an later to 2.00. The question is will those odds go to 1.95 or will those odds go back to 2.05. This is the market and you can try to speculate and get the best price. And if your price will be better than it is a closing line, then you will bet closing line. Pinnacle says, that if you can beat the closing line constantly, this is better indicator of who is a winning bettor than a profit. And if you beat the closing line, but you still didn’t make a profit, you were unlucky.

BETTING MODEL, VALUE AND MY WINNING PERCENTAGES

There are different approaches and different betting styles, some bettors will say, that only line movement is important, some other will say, that only statistics/betting models is important and a lot of bettors will say, that we don’t need to use statistics and analytics and they have special gift for betting. If we exclude the last type of betting, because I unfortunately don’t have super-human betting power and I believe most of you also don’t have this power, we need to to work and research games. And one thing is for sure:

One baseball game is an event, where two possible outcomes are possible. Home team win and away team win.

I have projected my winning percentages for both teams. How, You can learn here.

I have projected that Toronto Blue Jay should have 58.7% of chance to win this game and Boston Red Sox only 41.30% of chance. If the model is correct somehow, I see that I give Toronto more than 50% of chance, while bookmakers give them less than 50% of chance.

My fair odds on Toronto are set at 1.70 and this is basically direct price, that I expect. Or in other words, if my trust the model, I would expect, that I make a profit of $70 for my risked $100 if I bet and win with Toronto.

But we saw, that bookmakers offer me the odds of around 2.00 at this moment. They are willing to pay me more than I would expect. If I bet $100 on Toronto, I will make a profit of $100. Because of that I have a value on Toronto Blue Jays. Of course, if I would be quicker and if I would take Toronto earlier (my betting model is not dependent on line movement), I would get even better price. Some better or smarter bettors were faster and take Toronto for much better price. But what I like here is that the initial line movement and my betting model are on the same side.

TRADITIONAL ANALYSIS OF THE GAME

After we have information about the odds and after you have estimated your own winning percentages, we can make an extra analysis. I usually don’t like to write analysis, because if you want to write good and unique analysis, you need at least 45 minutes. Bettors then read this analysis and from delivering to final bets we lose couple of hours. And let’s check what can happen if you are 2 hours too late:

Because of writing analysis we probably lose the opening price of 2.11, and let’s say, that you I write analysis and I get the price of 2.06 and you get this analysis 2 hours later. Yes, this is exactly this example

04/23 11:10 PM 1.93 2.00 (get analysis)
04/23 09:38 PM 1.88 2.05
04/23 09:37 PM 1.91 2.02
04/23 09:26 PM 1.89 2.04
04/23 09:25 PM 1.88 2.05
04/23 09:24 PM 1.86 2.07
04/23 09:23 PM 1.87 2.06 (write analysis)
04/23 02:41 PM 1.85 2.08
04/23 01:56 PM 1.83 2.11 (opening price)

What that means in reality if you constantly get 2.00 instead of 2.06 on 1000 bets? And in Toronto vs Boston game we are talking about only 2 hours later.

Win% W L Odds 2 (+100) Odds 2.06 (+106)
47.50% 475 525 -50 -21.5
48.00% 480 520 -40 -11.2
48.50% 485 515 -30 -0.9
49.00% 490 510 -20 9.4
49.50% 495 505 -10 19.7
50.00% 500 500 0 30
50.50% 505 495 10 40.3
51.00% 510 490 20 50.6
51.50% 515 485 30 60.9
52.00% 520 480 40 71.2

In the best case (52% winning percentage) you can lose 31.2 units of profit on the long run, just because you didn’t make your own bets at the right time, but you read and follow other people’s picks. With $100 per unit, this is loss of $3120, just because of 2 hours late. Let’s go further. Betting $100 per game usually requires bankroll of $10.000, right? And losing $3120 on initial bankroll of $10,000 just because you are always too late, is little bit to much, isn’t it? Yes, of course every game is a different case and we can not use this example and implement it on every single baseball game, but I wanted to show you how much money you can lose because of writing and reading analysis of other people, instead of making your own.

But let’s get back to analysis of this game:

STARTING PITCHERS

The most important players in a baseball game are pitchers and because of them the odds changing on a daily basis, despite teams will play 3-4 games in a row against the same team.

Starting pitchers will start the game, which is 9 innings long (if we exclude extra innings if they happen). A lot of bettors will focus on ERA, which is not the best indicator of how good pitcher is, so I will show you couple of other things to check.

J.A. Happ is a lefty pitcher, who will start for Toronto and his ERA is 4.50 so far this season and this will tell us how many runs this pitcher allows per 9 innings. But ERA doesn’t include many other things and sometimes pitchers were just lucky. Imagine, that you have a pitcher with ERA 0, but he walked many hitters and he survived couple of bases loaded situation. Of course, ERA will be low, but on the long run, if he continues to play like this he will be hit hard. And ERA will not tell you this. ERA will tell you what happened, but we want to know what can possibly happen in the next game based on pitchers performance.

There are two very good sites like Fangraphs and Baseball-reference, where they provide some ERA metrics numbers and in this example we can check Happ’s xFIP (xFIP = ((13*(Fly balls * lgHR/FB%))+(3*(BB+HBP))-(2*K))/IP + constant). (fangraphs)

Rating FIP
Excellent 2.90
Great 3.20
Above Average 3.50
Average 3.80
Below Average 4.10
Poor 4.40
Awful 4.70

HAPP ERA = 4.50

HAPP xFIP = 2.97

As you can see in his example, despite his ERA is not that great his xFIP is almost excellent and xFIP for example is better future predictor than ERA. So with Happ we have very good pitcher on the mound.

He struck out 31 hitters in 22 innings this season, which is amazing. He went 5.5 innings per game, so we can expect that 3 innings will be pitched by bullpen and the biggest problem so far I see is that he allowed 5 home runs. This is the reason for big ERA.

What about his history against current Red Sox lineup?

BATTER AB HR R SO AVG OBP SLG OPS wOBA
J.D. Martinez R 10 0 2 0 0.3 0.417 0.3 0.717 0.342
Eduardo Nunez R 21 0 1 3 0.286 0.318 0.381 0.699 0.309
Christian Vazquez R 14 0 2 4 0.286 0.333 0.357 0.69 0.31
Rafael Devers L 6 0 0 2 0.333 0.333 0.333 0.667 0.3
Hanley Ramirez R 32 1 6 6 0.188 0.308 0.344 0.651 0.292
Mookie Betts R 26 1 3 2 0.192 0.222 0.423 0.645 0.272
Brock Holt L 9 0 1 2 0.222 0.3 0.333 0.633 0.285
Sandy Leon S 6 0 0 2 0.167 0.167 0.167 0.333 0.15
Jackie Bradley Jr. L 14 0 1 5 0.071 0.133 0.143 0.276 0.13
Andrew Benintendi L 10 0 0 2 0 0.167 0 0.167 0.117
Mitch Moreland L 5 0 1 2 0 0 0 0 0
Boston Red Sox: 153 2 17 30 0.196 0.266 0.301 0.567 0.255

As we can see, he has some very solid numbers against current Boston lineup, despite I would not pay such a big attention to those numbers because usually we have very small sample sizes and we must be careful how much weight we give.

However, Boston players combined 153 at bats against him with batting average of 0.196, which is also not most important metric for future predictions. But to get the first picture about how he played in the past against Boston Red Sox players we can check some numbers above in the table.

Happ is also 7-3 in his career against Boston and his team is 10-6 in those games. His career ERA versus Boston is 3.24.

Rick Porcello on the other side is a right handed pitcher, who will start for Boston. He is 4-0 to the season and he struck out 23 hitters in 25 innings. The most amazing thing about him so far is that he has amazing control. He walked only 1 hitter so far.

PORCELO ERA – 1.40

PORCELLO xFIP – 3.34

But if we check his xFIP numbers against Happ’s numbers, we see, that Happ has lower xFIP. At the end of the day, there will not be such a huge advantage and if we strictly stick with xFIP numbers, Toronto will have the advantage here.

Let’s check his numbers versus Toronto’s players:

BATTER AB HR R SO AVG OBP SLG OPS wOBA
Luke Maile R 1 0 0 0 1 1 2 3 1.25
Steve Pearce R 9 0 1 0 0.444 0.444 0.667 1.111 0.478
Justin Smoak S 30 3 6 8 0.267 0.389 0.633 1.022 0.428
Kevin Pillar R 36 0 3 7 0.278 0.316 0.389 0.705 0.311
Teoscar Hernandez R 3 0 0 1 0.333 0.333 0.333 0.667 0.3
Aledmys Diaz R 3 0 1 1 0.333 0.333 0.333 0.667 0.3
Devon Travis R 15 0 1 1 0.267 0.312 0.333 0.646 0.291
Kendrys Morales S 39 2 5 7 0.205 0.244 0.385 0.629 0.272
Curtis Granderson L 13 0 1 4 0.231 0.231 0.231 0.462 0.208
Russell Martin R 29 1 4 7 0.103 0.188 0.207 0.394 0.184
Randal Grichuk R 2 0 0 1 0 0.333 0 0.333 0.233
Active totals for
Toronto Blue Jays: 180 6 22 37 0.239 0.297 0.4 0.697 0.307

Toronto have 180 at bats against him, with batting average of 0.239 and 6 home runs.

Porcello is 9-8 against Toronto with ERA of 4.98 and his team is 10-10. Solid numbers, but if you strictly bet based on this pitcher vs batter information, then betting on Happ versus Boston would make you +5.2 units of profit in his career, while betting on Porcello would make you a loss of 0.9 units in his career versus Toronto.

The conclusion about pitchers: According to xFIP we have small advantage on Toronto’s side. Both pitchers have been playing very well so far and we will probably see very good pitching duel. Happ has pitched well against Boston in the past as well.

BULLPEN

The old times, when pitchers went 9 innings and at the same time scored 3 home runs are gone. Starting pitchers average is around 5.5 innings per game (my number is 5.46 innings per game for qualified SP last 365 days). If we know, that the game is 9 inning long, we simply can not ignore last 3-4 innings right? This is still more than 30% of the game.

When you make an analysis on bullpen (relievers), you can always check bullpen usage and if their key relievers pitched in a game the day before. But in our case, both teams had day off yesterday, so I will consider that all relievers, that are not injured are available today and can be used.

BOSTON Pitchers GP IP
Matt Barnes 1 1.1
Heath Hembree 1 1
Brian Johnson 1 0
Joe Kelly 1 1
Craig Kimbrel 0 0
Carson Smith 1 0.1
Hector Velazquez 1 3
Marcus Walden 0 0
Team total IP: 6.2

 

Pitcher GP IP
John Axford 2 1.2
Danny Barnes 2 2
Tyler Clippard 1 1
Aaron Loup 1 1
Tim Mayza 0 0
Seung-Hwan Oh 2 1.2
Roberto Osuna 1 1
Ryan Tepera 1 1
Team total IP: 9.1

I like to compare teams bullpens advanced ERA metrics numbers and according to my numbers I rank Blue Jays bullpen little bit better. I rank Blue Jays bullpen as 7th best and Boston as 16th best in the league (out of 30 teams).

TORONTO BULLPEN ERA – 2.12

TORONTO BULLPEN xFIP – 3.74

BOSTON BULLPEN ERA – 3.25

BOSTON BULLPEN xFIP – 3.83

As you can see, I would give small advantage here to Blue Jays.

HITTING AND THE OFFENSE

So, after we get the first picture about pitchers we need to check how many runs can our team score. Mainstream statistics that most sports fans and commentators use are home runs (HR), batting average (BA) and runs (R). Those are not the best future predictors and I see that more and more people started to talk about other metrics.

But let’s start with the basic picture about those two teams.

Boston scores 5.9 runs per game and they are one of the best offensive teams so far. Toronto on the other side scores 5.5 runs per game, which is also about league average. But instead of focusing on batting average you can focus on some other things, like  OBP, SLG, OPS, WRC+,…

And what I like to focus is on lefty vs righty matchups against hitters. In general, when left handed pitchers face left handed batters they have big advantage. It is much harder to hit left handed pitcher if you are a left handed batter. Second interesting thing is that there is much less left handed pitchers in the game and hitters sometimes are not that familiar with them. Of course this can not be the rule for every single player, but teams like to have at least one lefty in their rotation and at least one lefty in their bullpen so he can go against left handed batters in crucial moments.

According to my rankings I rank Boston as a 4th best offensive team in the league and Toronto as a 10th best team in the league. But this game is special because Boston will face a left handed pitcher and they were in 101 at bats this season against lefties. And what is interesting, I rank them as 29th team vs lefties so far. And today they will need to face Happ, that has almost excellent xFIP numbers and has very good past numbers vs Red Sox. Even if we check only runs/game, we see that Boston scores only 3.3 runs per game versus lefties, while on the other side they score much more vs righties. Note also, that Boston score little bit less on the road (4.7 compared to their overall 5.9 number)

Toronto on the other side score 5.5 runs per game, but they are much better versus right handed pitchers – 6.7 (this is the situation for them today) and they score more against lefties too – 5.8.

I rank Toronto as a 6th best team versus righties so far, while I rank Boston as a 29th versus lefties so far.

(of course I can not reveal everything in this post, but you can check my A Journey course, where I reveal exactly how I project my odds and what kind of metric and rankings I use)

FIELDING, ERRORS,…

The next thing you can do is to research couple of other things. How teams play in the field, how many errors they make, how good they are in stealing bases, etc…

BALLPARK FACTORS

We all know, that teams play in different ballparks and we have ballparks that are hitters friendly and ballparks that are pitchers friendly. In other words if Albert Pujols would play his whole career in Coors Field his numbers would be much better. I use ballpark adjusted numbers for my betting model. If you don’t use ballparks adjusted statistics, you can take this into account too.

Here is one interesting table from fantasypros, where you can get little bit better picture, which ballparks are hitters or pitchers friendly.

PARK NAME
RUNS
HR
1B
2B
3B
Coors Field
(Colorado Rockies)
1.378 1.223 1.195 1.267 1.816
Globe Life Park in Arlington
(Texas Rangers)
1.172 1.083 1.119 1.114 1.157
Chase Field
(Arizona Diamondbacks)
1.164 1.119 1.024 1.229 1.711
Fenway Park
(Boston Red Sox)
1.138 0.950 1.113 1.280 1.095
Progressive Field
(Cleveland Indians)
1.138 1.043 1.070 1.234 0.635
Oriole Park at Camden Yards
(Baltimore Orioles)
1.046 1.183 1.046 0.901 0.745
Target Field
(Minnesota Twins)
1.046 1.082 1.067 1.046 1.032
Miller Park
(Milwaukee Brewers)
1.045 1.178 0.941 1.070 1.000
Great American Ball Park
(Cincinnati Reds)
1.037 1.133 0.970 1.001 0.734
Kauffman Stadium
(Kansas City Royals)
1.031 0.795 1.007 1.183 1.398
Comerica Park
(Detroit Tigers)
1.028 1.025 1.011 0.996 1.485
Yankee Stadium
(New York Yankees)
1.025 1.301 0.942 0.856 0.613
Nationals Park
(Washington Nationals)
1.005 1.030 1.044 1.004 0.652
Rogers Centre
(Toronto Blue Jays)
0.993 0.978 0.948 1.142 1.000
Turner Field
(Atlanta Braves)
0.990 0.819 1.016 0.976 0.812
Wrigley Field
(Chicago Cubs)
0.985 1.004 0.971 0.933 1.135
Citizens Bank Park
(Philadelphia Phillies)
0.980 1.234 0.947 0.901 0.861
PNC Park
(Pittsburgh Pirates)
0.964 0.893 1.026 0.998 0.990
O.co Coliseum
(Oakland Athletics)
0.957 0.860 0.971 1.011 1.365
U.S. Cellular Field
(Chicago White Sox)
0.946 1.128 0.936 0.879 0.892
PETCO Park
(San Diego Padres)
0.922 0.919 0.981 0.956 0.835
Tropicana Field
(Tampa Bay Rays)
0.920 0.918 0.961 0.849 0.932
Safeco Field
(Seattle Mariners)
0.912 0.994 0.946 0.891 0.754
Busch Stadium
(St. Louis Cardinals)
0.912 0.870 1.043 0.957 0.835
Angel Stadium of Anaheim
(Los Angeles Angels)
0.907 0.982 0.989 0.855 0.682
Citi Field
(New York Mets)
0.900 0.941 0.889 0.892 0.655
Dodger Stadium
(Los Angeles Dodgers)
0.899 0.973 0.930 0.984 0.517
AT&T Park
(San Francisco Giants)
0.897 0.637 1.040 0.973 1.386
Marlins Park
(Miami Marlins)
0.879 0.795 0.960 0.867 0.967
Minute Maid Park
(Houston Astros)
0.850 0.972 0.926 0.880 1.107

 

UMPIRES

If you like, you can compare umpires and how they call strikes, balls, some of them favour home teams, etc. This is also another thing you can take into account.

INJURIES

It is of course important to take into account injuries. I use active rosters numbers, so the numbers from injured players are excluded and two notable missings are Donaldson fot Toronto (injured from 13.April) and Bogaerts for Boston (from 11.April).

CONCLUSION AND RECOMMENDED BET

So, what we have here?

The bookmakers opened the odds at 2.11 on Toronto, but the odds dropped to 2.00 later. We have my betting model, where I have projected, that Boston should be an underdog and not Toronto.

We have Happ, who has very good history against Red Sox players and his advanced ERA-metrics (I used xFIP for this example) are even better than Porcello’s numbers. A lot of bettors will be fooled by ERA numbers, because we have Porcello with much better ERA and on the first look Happ ERA of 4.5 looks bad versus a team, that scores 5.9 runs per game.

But we can not take this 5.9 runs per game for Boston numbers, because they score much less against left handed pitchers (3.3 runs) and they will face one very good left handed pitcher today.

Toronto is an underdog here, despite they will probably have better pitcher on the mound and as we saw, they have slightly better bullpen so far too.

Toronto is also very good scoring home team and they play well against right handed pitchers too. I don’t see any big advantage from Boston in this game and I think we have a good underdog play on Toronto.

I would recommend to take Toronto up to 1.95. 

If you want to learn how I project my odds, check this crazy offer, which will end on April 30.