There has been a lot said recently about the playoff system in Major League Baseball, and how the two teams in the World Series are not really the best teams in baseball. Some fans enjoy the high stakes playoff games where the entire season is on the line. Other fans prefer “fairer” scenarios where each team needs to play 1,000 regular season games to get the best representation of who has the best team.

A much stranger scenario is outlined in The Science of the Playoffs by Sky Andrecheck. Complicated scenarios are created to match a team’s playoff odds with how that team performed in the regular season to create a more just playoff system. For instance, if a team has a regular season record that indicates it has a 60% chance of being better than the team they are matched up against, they should be awarded a playoff scenario where they have a 60% chance of advancing. Although the sample sizes are small and do not give an ultimate answer to which team is better, this approach offers a “fairer” post-season solution based on a team’s regular season record. I decided to take this idea and run with it by figuring out if the Kansas City Royals’ postseason run exceeded the initial probability that their regular season record demonstrated of being the best team in the AL.

To determine the probability that one team is better than the one they are facing in the playoffs, I compared each team’s win total using a binomial distribution with unknown true win pcts (p in Binomial Distribution), but known win totals (k in Binomial Distribution). If a team is the better team, then their win pct would be better than the team they are up against. The probability that Kansas City is better than another team can be found by summing all possible probabilities where Kansas City has a particular win pct and the team they are facing has a lower win pct. The math behind this method is shown below comparing Kansas City to the Oakland Athletics. The same formula was also used to determine the probability that the Royals were better than the Angels and Orioles.

Additionally, I use the impact of home-field advantage in postseason calculations as giving the home team a 51% chance of winning an evenly matched series taken from here.

Going into the wild card playoff game, the Royals had only one more regular season win than the A’s. The regular season predicted that the Royals were the better team with a probability of 51.5%, slightly better than even money. Since the Royals were given home field advantage, they were awarded a probability of advancing close to what their regular season record demonstrated. The Royals won the game, fairly confirming that they should move on to the American League Division Series.

Next up for the Royals were the Los Angeles Angels. The Angels had a regular season record of 98 wins, much better than the Royals’ 89 win total. With this disparity in win totals, the Royals only had about a 14% chance of being the better team based on both teams’ regular season records. However, taking three out of three games from the Angels, two in Los Angeles and one in Kansas City, has about a 12% chance of happening if both teams are evenly matched. So, if the Angels were better, the probability of the Royals winning all three games would be even lower. The sweep exceeded the Royals’ initial probability of being better than the Angels, once again fairly pushing the Royals forward into the ALCS.

In the American League Championship Series, the Royals played the Orioles. The Orioles won 96 games, giving the Royals only about a 20% chance of actually being the better team. The probability of sweeping the Orioles in the ALCS if both teams were evenly matched was about 6%. Here, the Royals far exceeded their regular season odds of being considered the better team.

The odds of the Royal sweeping the entire American League in the playoffs exceeded the probability that the Royals were the best team. In other words, it was totally fair that the Royals won the AL pennant.

The Mets took a chance last year and inked Curtis Granderson, age 33, to a four-year contract worth $60 million. Granderson was just coming off an injury plagued season with the Yankees in which he fractured his right forearm, and then the pinky in his left hand, sidelining him for over 100 games. In 2013 he posted a slash line of .229/.317/.409. Prior to his 2013 season, Granderson finished 4th in MVP voting in 2011, and was an All-Star in 2011 and 2012, finishing with more than 40 HR and 100 RBI’s.

So what can we expect from Curtis Granderson for the rest of his career with the Mets? Is there hope that he will be the big clutch hitter the Mets desperately need and come close to his 2011 and 2012 seasons with the Yankees? Or will his name be forever remembered by Mets fans in the same category as Jason Bay and Chris Young, forged in the hall of ineptitude? Here is a look at Curtis Granderson’s numbers after 2010 when Granderson turned 29 and started his stint with the Yankees. Here is a look at some of his numbers from 2010-2012, before his injury-riddled 2013 campaign:

It is important to note that he is playing the majority of his games at notoriously hitter-friendly Yankees Stadium. Using a measure of the effect of Yankee Stadium called park index, it can found that Yankee Stadium has about a +3% increase on a left-hander’s average, and a +53% on a hitter’s home run total. Granderson hit 56 total homers at Yankee Stadium from 2010-2012. After the Mets reconfigured their outfield, their left-handed batters hit on average +2% more home runs. If we adjust Curtis Granderson’s home run total to playing at CitiField for these years, his adjusted home run total is somewhere between 26-27 per year.

This still is a great total, and I think any Met fan would welcome a 25+ home run season from Granderson with open arms. Right now there are 10 games left in the season and Granderson has 18 home runs. He could sit around 20 this season which would not be terrible unless we remember his atrocious .218/.320./.374 slash line. We also have to consider the unfortunate factor of Granderson’s age to this equation. Granderson has a little bit of a strange aging curve because of his incredible seasons at age 30 and 31. I decided to look at how similar players performed at ages 32, 33, 34, and 35 (no player that has a top-ten similarity score has played a season at age 36 yet). The similarity scores were calculated based on Baseball-Reference’s similarity scores equation.

All of my worst fears came true and I started having flashbacks of one of the all-time worst Mets busts as I saw the name that popped up at number 1 — Jason Bay. Here is what other similar players did at age 32, 33, 34, and 35 (I omitted information if a player played less than 70 games aside from Granderson’s season at age 32.):

This does not paint a good picture of what we hope to expect from Granderson. For a player signed to the amount of money as Granderson, I would like to see an OPS around or above .800. There are only two out of ten players — Phil Nevin and Jayson Werth, that hit decently at the advanced ages of 34 and 35 (Werth is hitting pretty well with over 80 RBI’s with an OPS above .800, Nevin hit decently with a 0.76 OPS and 22 home runs at age 35). Six out of ten players ended their careers following a tremendous decline before getting to age 34 (I included Jason Bay whose career was arguably over before age 31, a year after signing with the Mets), Edwin Encarnacion is too young to make any conclusions about, and it is looking like Jose Bautista will play well, or at least decently at ages 34 and 35.

Even though most similar players did not have good seasons, or even reach seasons at ages 34, 35, and 36, similar players like Jayson Werth, Phil Nevin, and Jose Bautista give us a glimmer of hope. Similar players in no way give us a definitive look at a player’s future, so there is also always the possibility Granderson carves himself a much different path than any of the players on this list. To determine what might be causing Granderson’s decline, I’m going to look through Granderson’s batted ball statistics along with walk rate and strikeout rate:

The most glaring discrepancy between Granderson’s time with the Mets and Yankees is his HR/FB rate. His BABIP has gone down a little, but it is not that far removed from his numbers from 2010-2012. BABIP is a good statistic to look at to determine if a player is having a relatively unlucky season by comparing it to that player’s normal BABIP. It looks like he might have been a little lucky getting hits in 2011. Other than that, BABIP does not tell the story of what has happened to Granderson in 2014.

My initial thought from watching Granderson play daily was that he is striking out at a much higher rate. In fact, his K% is lower than it was in 2011 and 2012, and on par with what it was in 2010. And here is where we come to his HR/FB. Although Granderson is hitting about the same FB%, the percent of his fly balls that are going out of the park is dismally low compared to how it was when he was hitting 40+ home runs at Yankee Stadium. Although this could partially be age-related, it could be easily argued that a huge component of this is also the change in ballpark where Granderson plays. It is hard to determine if Granderson could possibly change his approach somehow to adjust to CitiField’s landscape when he is going to be 34 years old next year. The future is looking bleak for Mets fans unless Granderson can figure out how to turn things around next season.