Wild Card season is only heating up as we near the end next week. Maybe we will know who will make it next week or maybe we will be left on a cliffhanger for the last game of the season. Today will focus on the chances of each team winning in home vs. road games since every win counts right now.
What the numbers mean: The negative number is how many fewer wins they have on the road vs. how many more wins they have at home. This was found by taking their home and away splits and plugging them into a formula for the answer.
-3.45 to 3.72
Magic Number: 2
Schedule: At home vs. Miami and San Francisco and finish on the road at Kansas City.
-1.74 to 1.87
Magic Number: 8
Schedule: At San Diego, then a homestand with Miami and Los Angeles.
-0.1 to 1.05
Schedule: Homestand vs. Chicago and Cincinnati, then on the road at St. Louis.
St. Louis Cardinals
-1.58 to 1.73
Schedule: At Pittsburgh, then a homestand with Chicago and Milwaukee.
New York Yankees
-2.83 to 3.09
Magic Number: 1
Schedule: At Toronto, then at home with Kansas City, Tampa Bay, and Toronto.
-1.01 to 0.93
Magic Number: 6
Schedule: At Detroit, then vs. Cleveland and Detroit at home.
Los Angeles Angels of Anaheim
-0.97 to 1.09
Schedule: At Houston and Chicago, then vs. Seattle at home.
-0.97 to 1.09
Schedule: At Oakland, then at home vs. Houston and Oakland.
May The Odds Be Ever In Your Favor.
Instead of doing simulations today, I decided to help you (and myself) further understand a confusing statistic that gets thrown around a lot. If you guessed WAR (Wins Above Replacement), then you are correct! If you already understand this, follow along to see if this is a good explanation. Here we go:
Football may be back, but there's a team doing something amazing in Cleveland right now and there is no way that will be ignored. Enjoy another great feat in baseball because this is a gift that keeps on giving.
This week, Chris Sale became the fastest to strike out 1500 times in MLB history, which calls for celebration. Sale is already a top candidate for the AL Cy Young and is stellar with a contending team that needed an ace in their rotation. Today's edition will honor and celebrate Sale and his accomplishment, so call today Sports Salerday instead.
Has Sale been this good his whole career? Answer your question here! Remember that he did not play too much in his first two seasons and was 21 when he was called up. He's been around and is great for most of his full seasons.
Sale finished third among all pitchers in 2015 with 274. The average of the other nine pitchers in the top 10 was 241.7.
How good is this year? This year stacks up closely with 2015 and he has all of September to match that record and smash it.
Remember z-scores from the past? Well, if you need a refresher, it's where your ability is put against the ability of others and divided by the standard deviation to see how much of an outlier your best is. In Sale's case, I decided to measure him up against all of his strikeout totals.
His Average: 188.5 Strikeouts per year
2015 Z-Score: .98
2017 Z-Score: .87
This means that his 2015 total was almost one standard deviation away, however his 2017 z-score is close (.11) and it would be great to see what it looks like at the end of the regular season.
Sale's next start is tomorrow, so try and watch him get closer to making his own history! See you then!
This week's edition will spotlight home runs (again!) but instead of focusing on one player, we will look at them as a whole. There will be no simulations but only graphs to show the differences over the years.
Thought that Aaron Judge's 496-ft. home run was the biggest moonshot you have ever seen? Well, it is one of the farthest in 10 seasons, but it is close to some from the past. Also, before Aaron Judge, there was Adam Dunn, who also was known for great home runs and strikeout rates, so he wins this round.
Hopefully this was a fun way to look at home runs! See you next week, baseball fans!
*These totals were taken on Friday at 12 pm PT, so data may be different today.
Giancarlo Stanton is heating up baseball right now with his six straight games with a home run and a career-high 44* home runs this year while it's still the middle of August and there's September left to do more damage.
Since he is doing so well in that department, let's take a look at how often we can expect him to hit a home run in every at-bat he takes. The best way to find this is to calculate confidence intervals that will show how less likely and how most likely he is to hit a home run.
Confidence Intervals and How They Work: Confidence Intervals try to estimate an athlete's ability to do a certain activity but they are not the end-all. The formula I used (yes, there are multiple for multiple occasions) works with single proportions, like home runs. These will predict a 95% chance of being correct and involve his observed proportion of success (44 HRs in 439 AB) add/subtracted by 2 (the multiplier used with an approximately normal distribution) that is multiplied by the square root of the performance (.100, or 44/439) multiplied by his performance subtracted by 1, over the number of attempts (439 again!!!). It sounds confusing in words, but it looks doable in number form. Thankfully, I can just show you the numbers and what his confidence intervals are.
Confidence Intervals for 2017:
We are 95% confident that the interval of plausible values from .072 to .128 contain Stanton's ability to hit a home run this season.
Now, let's compare this to another season of this, because the best person to compare him to is himself and not anyone else. I chose 2014 because he hit 37 that year, his previous career-high. 37 is a great total by itself, but what he is doing this year makes that look mediocre now once you see the difference below.
Confidence Intervals for 2014:
We are 95% confident that the interval of plausible values from .046 to .09 contain Stanton's ability to hit a home run in 2014.
Conclusion: Stanton is amazing and should not stop hitting balls out of the park because we love it. Seriously, it would be great to see this again once the season is over and if it changed by a lot or only a little.
Note: These stats were taken on Friday morning before his game vs. the Mets last night.
While the highest number of stolen bases is 27 (Jarrod Dyson) in the American League, the National League is speeding along with 45 (Billy Hamilton). With the gap of 18 bases between the two, you may think that the National League is superior to the American, but the only way that can be proved or disproved is by using statistics, like means.
If you compare the means of 10 of the top runners in each league, it will involve a test statistic and the test simulations that will see if the test statistic is significant or not.
Test Statistic: 2.1
NL Mean: 23.9
AL Mean: 21.8
Chance of getting this difference: 51%
What that means: No greater ability in National League to steal bases.
*Chances were determined in 200 trials.
Test Statistic: 3
AL Median: 21.5
NL Median: 18.5
Chance of getting this difference: 46%
What that means: No greater ability in National League to steal bases.
*Chances were determined in 100 trials.
Everything is the same with 10 players, but if we add more, then we may see different results. Future project, anyone? You may see this again, so stay tuned!
Every wondered if pitchers have solved striking out the best players by just walking them too often? Some people do believe that by walking a good hitter often enough, they will start to lose their ability to hit well enough.
This issue came up a few seasons ago when Bryce Harper was slumping due to a higher number of walks. Recently with the slump of Judge, I wondered if one of the most walked players in the MLB was going through that same thing.
By using the number of walks and the number of strikeouts for the top players in each league that season, I tried to find the correlation of it all. This all leads to the question of: Does a higher number of walks lead to a high number of strikeouts?
2016- Mike Trout (116 W, 137 SO)
Paul Goldschmidt (110 W, 150 SO)
2015- Joey Votto (143 W, 135 SO)
Jose Bautista (110 W, 106 SO)
2014- Carlos Santana (113 W, 124 SO)
Matt Carpenter (95 W, 111 SO)
2013- Joey Votto (135 W, 138 SO)
Mike Trout (110 W, 136 SO)
2012- Adam Dunn (105 W, 222 SO)
Dan Uggla (94 W, 168 SO)
2011- Jose Bautista (132 W, 111 SO)
Joey Votto (110 W, 129 SO)
This is called small negative association, where the r is negative and below -0.3. The actual graph was listed at -0.246, so it fits the bill. This means that for every increase in one area, there is an decrease in the other and vice versa. So, for every walk there is actually a decrease in strikeouts. I guess we were wrong on this one.
The simulation sees how common it is for the small negative association to occur and it is actually very common. Therefore, we cannot conclude that an increased number of walks creates an increased number of strikeouts. But, since the pitchers like to do that to the best players, let them keep thinking that it works.
Hope that this debunked some previous hunches about walks!
The Dodgers have made July their month by going 18-3, a fantastic .857 winning percentage, and hold the best record in baseball at 72-31. This month has been historic for the already historic franchise so let's go over some stats:
On Wednesday they reached the earliest time in franchise history where they were 40 games over .500.
Even when they are not dominating in a game, they are an MLB-best 29-31 when trailing.
Their win on Monday rank them fourth in most wins in 100 games in the Expansion Era that dates back to 1961 (all from ESPN Stats and Info).
So, it's time for us to put their month to the test and see if they were streaky in July or not. Let's go over the rules again...
What qualifies as streaky? Well, since there are less than 50 games, we will see how many times a streak of 3 or more occurred. We will do this with a simulation that counts how many times a streak of 3+ happens and if a majority of the dots are 3+, then they are not streaky.
SO, the magic number to be streaky is to have 5% or less of dots to be 3+ because that means that what they are doing is truly special.
If it is above, then they are not streaky and their game results are independent from each other. So that means that every game is new chance to win and to not build off from the day before.
Results from all of July up until last night's win are used for the simulation below that consisted of 200 trials.
Longest Streak= 11 wins
Streaks of 3+= 2
They may not be classified as 'streaky' here but they still have all those wins in July and you cannot take those away from them. Let's see if they can continue their winning ways as the month fades out into August.
With all the increased home run activity going on in baseball right now, might as well check to see which league is superior. The obvious answer would be the American League due to Aaron Judge winning the Home Run Derby but there is the chance that both leagues may be more similar than we thought.
Through comparing the two means of each league in different categories, we will find out who is the better league (or maybe they're equal!).
How This Works: I looked up the Home Runs per Game and Average True Distance for both leagues on hittrackeronline.com to serve as the data. The differences between each mean/average were used as the test statistic which layed the groundwork for what we were looking for. If the test statistic occured more than 5% of the time in 400 trials, the abilities in each league are the same (yay for equality!). If it occured less than 5%, then one has a better ability than the other.
Difference in HRs per Game
AL Average: 2.61 HRs
NL Average: 2.43 HRs
Test Statistic/Difference: 0.18
Verdict: Teams in the AL have the same ability as teams in the NL and the increased home runs per game is due to random chance.
Difference in Average True Distance
AL Average: 401.0
NL Average: 400.6
Test Statistic/Difference: 0.4
Verdict: Teams in the AL have the same ability as teams in the NL in terms of hitting longer home runs and the slightly increased performance is due to random chance.
Looks like no one is super special yet, but hey! There's still more to come this season regarding home runs! Make sure to stay tuned and we will see you soon!
Note: Trials were conducted on Thursday night so the stats may be slightly different now.
Hi, I'm Jenna and I'm a sports fan! I've been avidly watching sports since 2011 because I found that by watching sports, I would be able to communicate with my dad and brother better. Ever since I got into sports, I've been able to enjoy myself more when I go to sporting events with my family.