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.
Ever wondered if the first half could easily tell you who the winner of the World Series is? Find out today if there IS actually a correlation between the first half and the second. By using the split records from the past ten winners, we will see if the first half correlates to second half success and also compare today's top teams to yesterday's.
First Half Statistics*
Average Winning Percentage- .579
Median Winning Percentage- .585
Least Amount of Wins- 46 by San Francisco Giants in 2012 (.523 Winning %)
Most Amount of Wins- 58 by Boston Red Sox in 2013 (.659 Winning %)
2016: Chicago Cubs (53-35)
2015: Kansas City Royals (52-34)
2014: San Francisco Giants (53-43)
2013: Boston Red Sox (58-39)
2012: San Francisco Giants (46-40)
2011: St. Louis Cardinals (49-43)
2010: San Francisco Giants (47-41)
2009: New York Yankees (51-37)
2008: Philadelphia Phillies (48-33)
2007: Boston Red Sox (53-34)
*Compiled from past 10 World Series Winners
Second Half Records
2016: Chicago Cubs (50-23)
2015: Kansas City Royals (43-33)
2014: San Francisco Giants (35-31)
2013: Boston Red Sox (39-26)
2012: San Francisco Giants (48-28)
2011: St. Louis Cardinals (41-29)
2010: San Francisco Giants (45-29)
2009: New York Yankees (52-22)
2008: Philadelphia Phillies (44-37)
2007: Boston Red Sox (43-32)
Data Plot & Results
From what we see here, there is not a direct correlation between first and second half. That means that doing well in the first half does not mean you will do well in the second and win the World Series. All that matters really is for your team to outlast the posteason and the grueling schedule.
Past to Present
IF Houston or Los Angeles win it all then it would be the first time in ten years that a team with 60 or more wins before the All-Star break won the World Series.
The team closest to the average winning percentage would have to be Boston at .568 and if history repeats itself, Boston could bring back another one.
None of the division-leading teams could beat San Francisco's record of 46 wins even though Cleveland came close with 47 wins. Missed it by that much.
But if Minnesota, Atlanta, Chicago, and Texas won the World Series, they could break the first half record.
Hope this helped you understand more about first half success and how it does not relate to the second half but can be helpful.
The All-Star break is fast approaching, so it's about time for us to get prepared for the always fun Home Run Derby. This week we will look over confidence intervals and see which player has the highest confidence to hit the most home runs. All of this is based on this season's numbers, so the career numbers do not count (works better for the rookies).
Remember that confidence intervals only measure 95% of our confidence in their ability. There is a margin of error and this is not the know-all for the winner. I will predict that on Monday when we debrief who is participating in a deeper way.
20.5 HRs to 25.6 HRs
Home Runs: 23
11.24 HRs to 14.76 HRs
Home Runs: 13
22.18 HRs to 27.8 HRs
Home Runs: 25
17.71 HRs to 22.29 HRs
Home Runs: 20
21.02 HRs to 26.9 HRs
Home Runs: 24
16.14 HRs to 19.85 HRs
Home Runs: 18
25.65 HRs to 32.34 HRs
Home Runs: 29
16.7 HRs to 21.3 HRs
Home Runs: 19
Looks like Aaron Judge is the one to beat here with the best confidence intervals. Fear not, there is more to be tested in order for Judge to be considered the clear choice as the winner. Stay tuned and enjoy these stats because more are coming!
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.