1. The Baltimore Orioles, Relationship of Wins and Loses, Batting Average, Earned Run Average, and Errors
Stalanic Anu, Matthew Beeman, Jonathon Chudoba, Tony MC, Laura Williams

2. Why We Chose This Topic
Sports are something that people follow with a passion looking at every single detail. Stats and data for teams along with players is kept and analyzed in order ensure the highest level of skill is brought to the game. With so many people watching sports for the high level of competition and skill, every stat no matter how ridiculous is kept. This allows the perfect opportunity to look at sports and the data for a statistical analysis.
The Baltimore Orioles, a professional baseball team in the MLB, have been rebuilding their organization for the last couple of years. The Orioles had an average season last year with a record and and hope improve their record this year. With the 2016 season finally underway, we wanted to look at the batting average, earned runs average, and errors of last season to see if that played a part in their wins and losses. So we are attempting to show if there is a correlation between earned run average with the wins and losses, along with the relationship between batting average and errors by position of the Baltimore Orioles in 2015.

3. Introduction
For our project we looked at the Wins and Loses of the Baltimore Orioles, along with the team’s batting averages, errors, and Earned Run Average (ERA) over the season.
We then subcategorized the positions into a collective group. 1B, 2B, SS, and 3B are included as infield (IF). LF, CF, and RF are included as outfield (OF). Catchers are included as catchers, and Designated Hitters are included as DH. We also categorized pitchers as Starting Pitchers (SP), Relief Pitchers (RP), and Closers.

4. Wins vs. Loses Over a Season
We created a bar graph the shows the wins and loses over a season.

5. Batting Averages By Month
We created a bar graph the show the batting averages per month

6. RS vs. RA
We created a scatter plot to breakdown the relationship between runs scored per month and runs scored against per month.
RUNS FOR
April-112
May-95
June-148
July-93
August-119
Sept/Oct-146
RUNS AGAINST
April-104
May-104
June-102
July-90
August-131
Sept/Oct-162

7. We created a bar graph to show the batting average by position for the season.

8. We created a bar graph to show errors by position for the season.

9. We created a line graph to show the ERA by type of pitcher for the season.

10. Hypothesis
Null Hypothesis- There is no correlation between earned run average on the win percentage of the Baltimore Orioles in 2015.
Alternate Hypothesis- There is a correlation between the earned run average, on the win percentage of the Baltimore Orioles in 2015.

11. Mean, Median, Mode Mean Median Mode Variance Standard Deviation
Batting Avg
Count:15
Sum: 3.703
0.246
0.247
0.237
0.281
Earned Run Avg
Count: 17
Sum: 65.4
3.847
4.11
4.25 & 4.91
2.240
1.452
Erorr
Count:48
Sum:77
1.6041 1
10.414
3.193

12. Correlation
We created a scatter plot to show the correlation between a pitcher’s Earned Run Average (ERA) and the number of wins that the pitcher was responsible for.

13. Probability
In the 2015 season, the Orioles won 81 games and also lost 81 won games. The probability of the Orioles winning is 50% and the probability of them losing is also 50%.
The next probability that we calculated was “Out of the 7 starting pitchers for the orioles how many different rotations could there have been?”
7x6x5x4x3x2x1= 5,040
This calculation means that there are 5,040 different rotations that the manager could possibly select.

14. Conclusion
There is a relationship between Batting Average, Errors, and Earned Run Average on the Baltimore Orioles Win Percentage, but there is no correlation between just the Earned Run Average and Win Percentage.