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## 2014 MLB Season Preview

27/12/2013

By Harri Kairavuo | eastsideportsanalysis.com

In this season preview I am discussing the MLB-season 2014 from a betting perspective. I try to give a straightforward picture of the things I feel are important, in order for the season to be profitbale. I am also going through the differences in my handicapping methods compared to last season. With the changes I’ve made I am looking to correct the mistakes that were made last season (eg. a weak start of the season and very bad picks for underdogs). The reader of this text should make note, that I am only presenting my own opinions. The absolute truth of the discussed topics is probably not known by anyone.

MLB is one of the most difficult leagues to be succesful in for a bettor. It’s together with NFL one of the most analysed leagues in the world, mainly because of the nature of the game (baseball is as sport where it’s pretty easy, compared to other sports, to measure the difference in ability between teams). Another reason is that there is a very active community of baseball analysts (sabermetricians) online, which have made stats and research easy to access. The accessibility of stats and research is probably one important reason, why there is so much analysis available. To be a profitable MLB-handicapper you have to be able to beat a very efficient betting-market. The markets are efficient because of the huge analysing machinery, which helps bettors assess the current ability between teams. Because of this the odds can be expected to reach a proper level pretty often. This said, there’s still a lot of opportunities to be exploited on a daily basis, even if the largest EV+ bets might not be found in MLB. To be able to perform in an environment like this you have to do the groundwork at least as well as everybody else, and then find your own niche within the analysis, which you do better than everybody else.

I aim to do my analysis systematically and in the same way every round (day). I’ve been using a lot of time to build an efficient platform, so that the basic every day analysis, wouldn’t take too much time. With basic analysis I am referring to the process, which starts with the daily stats update and ends with the simulation of the games. Between these two steps the stats are running through my model, which creates probabilities for all different batting/pitching outcomes for every single player.

Since the season 2006 the average runs per game (RPG) has declined from 9.7 RPG to 8.3 RPG in 2013. Based on my own projections I believe that this trend will continue this season. The average RPG won’t probably fall dramatically, from an already pretty low level in 2013, but I still expect that the RPG will be lower in 2014. This is a very important thing to consider when handicapping games, if the league average RPG is a parameter in the model.

I expect more Runline-picks in 2014 than in 2013 which likely means, that there will be less Moneyline-picks. Runline-bets are a bit more interesting from a handicapper’s point of view because they are a bit more difficult to handicap than Moneyline-bets, and therefore it should be, at least theoretically, easier to find value from them. Runline-bets are more difficult to evaluate because in addition to the measurement of the teams relative strength, the run-environment of the game should also be measured. For example +1.5 runs for the away team could be worth more/less depending on in what kind of run environment (ballpark size, weather etc.) the game is played. This is a part of my handicapping methods which I’ve put a lot of effort in because I feel, that it’s where I’ve got the biggest probability to have an edge over the rest of the board.

Simulation is a new method, which I am going to use this upcoming season. In 2013 I used the Pythagorean Expectation* formula to calculate probabilities. With this model you first estimate the number of runs scored for both the home- and away team and after that you use the formula to calculate the probabilities. The forthcoming season I am going to replace these steps by simulating the games (every single plate appearance, which is every single matchup between the pitcher and batter). When there is enough simulated plate appearances (about 3.84 million simulations) it is possible to build a score-matrix, that should reflect the probability of different results of the game. If the individual player abilities are estimated right, it is possible to make better estimations of the outcome of the game with this method than with the method I used previously.

The start of the season 2013 was pretty rough for my picks. The reason to the underperformance can very likely be found in the model I used, which gave a too large weight to short trends (stats from previously played games). I’ve done some changes (improvements) to my model for this season and for example dropped the shortest trend-parameters. I’ve also revised my model, with the aim to better model the environment in the beginning of the season. In addition I am going to use external player analysis as a small part of my handicapping. The biggest change for 2014, in addition to using simulation, is a relief pitcher-engine I’ve created. Previously, I’ve evaluated relief pitchers as a group and not as individual players, but with the help of the relief pitcher-engine, I am able to project the most probable relief pitchers for a single game. Because the relievers usually pitch 1/3 of a game and usually the most critical innings, I found it of the highest priority, to be able to evaluate this part as accurately as possible.

The picks for underdogs (estimated probability < 50 %) were also bad in 2013. The number of these picks were pretty low (172 picks) and therefore I won’t make bigger conclusions of them at this stage as I’ve done with the beginning of the season bets. I’ll have them under special observation in the beginning of the season and they’re going through a couple of extra steps, before I decide whether I am going to publish them.

I’ve also made minor changes to the betting model (wagering) by adding bet sizes 1.5 and 2.5 units (previously 1-, 2- and 3 units).

The picks are going to be free of charge until the All-Stars break. After that (18.7.2014 ->) I am going to start to charge for them. My goal is to maintain a customer friendly betting service, where bettors can buy winning picks. For the season 2014 I am aiming for a 104 % rate of return. If this level is achieved, and if there is a few less picks than in 2013 (around 900 picks), the total return (with an average bet size of 2 units) would be +72 units. The performance for 2013 can be found here.

In this season preview I am discussing the MLB-season 2014 from a betting perspective. I try to give a straightforward picture of the things I feel are important, in order for the season to be profitbale. I am also going through the differences in my handicapping methods compared to last season. With the changes I’ve made I am looking to correct the mistakes that were made last season (eg. a weak start of the season and very bad picks for underdogs). The reader of this text should make note, that I am only presenting my own opinions. The absolute truth of the discussed topics is probably not known by anyone.

**MLB- The most difficult league to handicap?**MLB is one of the most difficult leagues to be succesful in for a bettor. It’s together with NFL one of the most analysed leagues in the world, mainly because of the nature of the game (baseball is as sport where it’s pretty easy, compared to other sports, to measure the difference in ability between teams). Another reason is that there is a very active community of baseball analysts (sabermetricians) online, which have made stats and research easy to access. The accessibility of stats and research is probably one important reason, why there is so much analysis available. To be a profitable MLB-handicapper you have to be able to beat a very efficient betting-market. The markets are efficient because of the huge analysing machinery, which helps bettors assess the current ability between teams. Because of this the odds can be expected to reach a proper level pretty often. This said, there’s still a lot of opportunities to be exploited on a daily basis, even if the largest EV+ bets might not be found in MLB. To be able to perform in an environment like this you have to do the groundwork at least as well as everybody else, and then find your own niche within the analysis, which you do better than everybody else.

I aim to do my analysis systematically and in the same way every round (day). I’ve been using a lot of time to build an efficient platform, so that the basic every day analysis, wouldn’t take too much time. With basic analysis I am referring to the process, which starts with the daily stats update and ends with the simulation of the games. Between these two steps the stats are running through my model, which creates probabilities for all different batting/pitching outcomes for every single player.

**What to focus on in 2014?**Since the season 2006 the average runs per game (RPG) has declined from 9.7 RPG to 8.3 RPG in 2013. Based on my own projections I believe that this trend will continue this season. The average RPG won’t probably fall dramatically, from an already pretty low level in 2013, but I still expect that the RPG will be lower in 2014. This is a very important thing to consider when handicapping games, if the league average RPG is a parameter in the model.

I expect more Runline-picks in 2014 than in 2013 which likely means, that there will be less Moneyline-picks. Runline-bets are a bit more interesting from a handicapper’s point of view because they are a bit more difficult to handicap than Moneyline-bets, and therefore it should be, at least theoretically, easier to find value from them. Runline-bets are more difficult to evaluate because in addition to the measurement of the teams relative strength, the run-environment of the game should also be measured. For example +1.5 runs for the away team could be worth more/less depending on in what kind of run environment (ballpark size, weather etc.) the game is played. This is a part of my handicapping methods which I’ve put a lot of effort in because I feel, that it’s where I’ve got the biggest probability to have an edge over the rest of the board.

**Changes for 2014**Simulation is a new method, which I am going to use this upcoming season. In 2013 I used the Pythagorean Expectation* formula to calculate probabilities. With this model you first estimate the number of runs scored for both the home- and away team and after that you use the formula to calculate the probabilities. The forthcoming season I am going to replace these steps by simulating the games (every single plate appearance, which is every single matchup between the pitcher and batter). When there is enough simulated plate appearances (about 3.84 million simulations) it is possible to build a score-matrix, that should reflect the probability of different results of the game. If the individual player abilities are estimated right, it is possible to make better estimations of the outcome of the game with this method than with the method I used previously.

The start of the season 2013 was pretty rough for my picks. The reason to the underperformance can very likely be found in the model I used, which gave a too large weight to short trends (stats from previously played games). I’ve done some changes (improvements) to my model for this season and for example dropped the shortest trend-parameters. I’ve also revised my model, with the aim to better model the environment in the beginning of the season. In addition I am going to use external player analysis as a small part of my handicapping. The biggest change for 2014, in addition to using simulation, is a relief pitcher-engine I’ve created. Previously, I’ve evaluated relief pitchers as a group and not as individual players, but with the help of the relief pitcher-engine, I am able to project the most probable relief pitchers for a single game. Because the relievers usually pitch 1/3 of a game and usually the most critical innings, I found it of the highest priority, to be able to evaluate this part as accurately as possible.

The picks for underdogs (estimated probability < 50 %) were also bad in 2013. The number of these picks were pretty low (172 picks) and therefore I won’t make bigger conclusions of them at this stage as I’ve done with the beginning of the season bets. I’ll have them under special observation in the beginning of the season and they’re going through a couple of extra steps, before I decide whether I am going to publish them.

I’ve also made minor changes to the betting model (wagering) by adding bet sizes 1.5 and 2.5 units (previously 1-, 2- and 3 units).

**Goals**The picks are going to be free of charge until the All-Stars break. After that (18.7.2014 ->) I am going to start to charge for them. My goal is to maintain a customer friendly betting service, where bettors can buy winning picks. For the season 2014 I am aiming for a 104 % rate of return. If this level is achieved, and if there is a few less picks than in 2013 (around 900 picks), the total return (with an average bet size of 2 units) would be +72 units. The performance for 2013 can be found here.

*http://en.wikipedia.org/wiki/Pythagorean_expectation