scholarly journals The Prediction of Batting Averages in Major League Baseball

Stats ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 84-93 ◽  
Author(s):  
Sarah R. Bailey ◽  
Jason Loeppky ◽  
Tim B. Swartz
Keyword(s):  
Linear Regression ◽  
Major League Baseball ◽  
Difficult Problem ◽  
Standard Errors ◽  
Simple Linear Regression ◽  
Empirical Comparison ◽  
Batting Average ◽  
Major League ◽  
Test Algorithm

The prediction of yearly batting averages in Major League Baseball is a notoriously difficult problem where standard errors using the well-known PECOTA (Player Empirical Comparison and Optimization Test Algorithm) system are roughly 20 points. This paper considers the use of ball-by-ball data provided by the Statcast system in an attempt to predict batting averages. The publicly available Statcast data and resultant predictions supplement proprietary PECOTA forecasts. With detailed Statcast data, we attempt to account for a luck component involving batting averages. It is anticipated that the luck component will not be repeated in future seasons. The two predictions (Statcast and PECOTA) are combined via simple linear regression to provide improved forecasts of batting average.

scholarly journals The Impact of Temperature on Major League Baseball

2013 ◽  
Vol 5 (4) ◽  
pp. 359-366 ◽  
Author(s):  
Brandon Lee D. Koch ◽  
Anna K. Panorska
Keyword(s):  
United States ◽  
Major League Baseball ◽  
The United States ◽  
Cold Weather ◽  
Baseball Game ◽  
Batting Average ◽  
Warm Weather ◽  
Major League ◽  
National League ◽  
The Impact

Abstract Major League Baseball is played from the beginning of April through the end of October each year, encompassing three of the four meteorological seasons: spring, summer, and fall. The 30 teams play in cities across the United States and Canada in many types of weather. This work studies the impact of temperature on a Major League Baseball game by examining the association between temperature and several Major League Baseball game statistics, including runs scored, batting average, slugging percentage, on-base percentage, home runs, walks, strikeouts, hit-batsmen, stolen bases, and errors. Data from 22 215 games, spanning the 2000–11 regular seasons, were studied. Temperature was categorized as “cold,” “average,” and “warm.” Analyses were performed on the following populations: all Major League Baseball games, games played in the National League, games played in the American League, and games played in 23 different stadiums that are currently being used by Major League Baseball teams. Home and away teams' performances were analyzed separately for each population of games. The results of this study show that runs scored, batting average, slugging percentage, on-base percentage, and home runs significantly increase while walks significantly decrease in warm weather compared to cold weather.

scholarly journals Home Run Derby Participation in Major League Baseball Players: Is There Associated Injury Risk and Impact on Second-Half Performance?

2021 ◽  
Vol 9 (2) ◽  
pp. 232596712098335
Author(s):  
Steven F. DeFroda ◽  
Steven J. Staffa ◽  
Tim Keeley ◽  
Peter K. Kriz
Keyword(s):  
Linear Regression ◽  
Injury Risk ◽  
Major League Baseball ◽  
Linear Regression Analysis ◽  
Control Group ◽  
Level Of Evidence ◽  
Major League ◽  
Maximum Effort ◽  
Multivariable Linear Regression Analysis ◽  
The Impact

Background: The Major League Baseball (MLB) All-Star Game (ASG) Home Run Derby (HRD) remains a highly anticipated event, during which contestants can take hundreds of maximum-effort swings en route to hitting a multitude of home runs. Critics have openly questioned the risk-benefit of HRD participation as it pertains to injury, alterations in swing mechanics, and timing. Purpose: To determine whether participation in the MLB ASG HRD was associated with both increased injury risk and decline in second-half performance in MLB players. Study Design: Cohort study; Level of evidence, 3. Methods: MLB players who participated in the HRD between 2006 and 2019 were identified through publicly available internet databases. A control group of ASG participants who had the highest home run totals in the first half of the corresponding MLB season were selected as a control group. Multivariable linear regression was used to determine independent associations between HRD participation and batting metrics in the second half of the season. Multivariable logistic regression also assessed the impact of HRD participation on injured list placement during the second half of the concurrent MLB season. Results: A total of 114 HRD participants and 114 ASG participant controls competed during the study period. No statistically significant differences were seen in batting metrics in the second half of the MLB season between HRD participants and ASG controls, although HRD participants had a significantly lower wins-above-replacement statistic for the season compared with controls (4.69 ± 2.06 vs 5.33 ± 2.08; P = .021). HRD participation was not significantly associated with injury during the second half. The number of HRD rounds in which a player participated did not result in a statistically significant increased odds of injury during the second half of the MLB season. Conclusion: HRD participants did not have increased odds of being placed on the injured list during the second half of the MLB season compared with controls, nor did they experience second-half performance declines in offensive production versus controls when multivariable linear regression analysis was performed.

Swing shift: a mathematical approach to defensive positioning in baseball

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Elizabeth Bouzarth ◽  
Benjamin Grannan ◽  
John Harris ◽  
Andrew Hartley ◽  
Kevin Hutson ◽  
...  
Keyword(s):  
Decision Making ◽  
Historical Data ◽  
Major League Baseball ◽  
Mathematical Approach ◽  
Entire Field ◽  
Batting Average ◽  
Link Type ◽  
Major League ◽  
Decision Making Tool

AbstractDefensive repositioning strategies (shifts) have become more prevalent in Major League Baseball in recent years. In 2018, batters faced some form of the shift in 34% of their plate appearances (Sawchik, Travis. 2019. “Don’t Worry, MLB–Hitters Are Killing The Shift On Their Own.” FiveThirtyEight, January 17, 2019. Also available at fivethirtyeight.com/features/dont-worry-mlb-hitters-are-killing-the-shift-on-their-own/). Most teams use a shift that overloads one side of the infield and adjusts the positioning of the outfield. In this work we describe a mathematical approach to the positioning of players over the entire field of play without the limitations of traditional positions or current methods of shifting. The model uses historical data for individual batters, and it leaves open the possibility of fewer than four infielders. The model also incorporates risk penalties for positioning players too far from areas of the field in which extra-base hits are more likely. This work is meant to serve as a decision-making tool for coaches and managers to best use their defensive assets. Our simulations show that an optimal positioning with three infielders lowered predicted batting average on balls in play (BABIP) by 5.9% for right-handers and by 10.3% for left-handers on average when compared to a standard four-infielder placement of players.

The Contribution of Sexual Identity Towards the Students’ Perception of Lesbian, Gay, Bisexual, and Transgender

10.32698/0642 ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 120
Author(s):  
Wiwi Delfita ◽  
Neviyarni S. ◽  
Riska Ahmad
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Sexual Identity ◽  
Student Perceptions ◽  
Undergraduate Students ◽  
Linear Regression Analysis ◽  
Sampling Technique ◽  
Simple Linear Regression ◽  
Simple Linear Regression Analysis ◽  
Bisexual And Transgender

Some students perceive lesbian, gay, bisexual, and transgender (LGBT) positively, even though LGBT is a sexual deviation that is not appropriate with values and norms. There are several factors that influence an individual's perception of LGBT, including sexual identity. This study aims at looking at the contribution of sexual identity to student perceptions about LGBT. This research used a quantitative approach with a descriptive method and a simple linear regression analysis. The sample of this research was 385 taken from 15.752 undergraduate students of Universitas Negeri Padang which the sample was drawn by using the Slovin formula and continued with a Proportional Random Sampling technique. The instrument used was the Guttman model's sexual identity scale and the scale of students' perceptions of the LGBT Likert model. After analyzing the data with the descriptive technique and the simple linear regression analysis, the results showed that sexual identity significantly contributed to the students' perceptions of LGBT. This research has implications as a basis for counselors to help students avoid sexual identity mismatches and prevent the emergence of positive perceptions of LGBT.

Pengaruh Penghargaan Terhadap Kinerja Karyawan pada CV. Dino Manunggal Karsa Bogor

2019 ◽  
Vol 4 (2) ◽  
pp. 17
Author(s):  
Dedy Mulyadi ◽  
Didik Purwanto
Keyword(s):  
Linear Regression ◽  
Performance Assessment ◽  
Correlation Coefficient ◽  
Linear Regression Analysis ◽  
Strong Relationship ◽  
Simple Linear Regression ◽  
New Employees ◽  
Company Policy ◽  
Effective Manner ◽  
Relationship Of

The question of compensation in addition to sensitive to be driving someone to worl due to an effect on morale and discipline employees. Therefore , any  agency or any organization should be able to provide compensation equal to the workload  to create a workforce that efficient and effective manner can be realized. Amaore than that, the company’s goal to improve performance. Performance assessment is a subjective process that involves human judgments. Thus, performance assessment is very likely wrong and very easily influonced by sources that are not actual, so it must be taken into account and considered reasinable. Frformance appraisals are considered  to meet the target if it has a good impact on new employees who rated their performance. Simple linear regression analysis using SPSS version 12:00 data processing obtained tegression equation Y = 0,487 X 74 + with an explanation of X = award, 74 = constant, 0.487 = coefficient awards, and Y = performance based on simple linear regression equation in case of increase of one unit of the  performance award will be increased 0.487 units. If company policy negates the performance award will remain at a constant rate (74) units . (A) Test results obtained thitung significant constants of (12.574) > t table for (1.960 then reject Ho constanta significant meaning. (B) significant Test award coefficient t count the results obtained by (2.164)> t table foe (1.96) then reject Ho the mean coeffent of appreciation affect the performance . (C) correlation coefficient analysis is done by calculating the product moment corration (pearson)  to test  whether or not a strong  relationship between the variables X  dan Y , based on the results of cakculations with SPSS  table valuse obtained by calculating the  correlation coefficient r (0.3100> r on the table for a = 0,05 (0.291) then reject Ho, which means there is a relationship of respect for performance. When we enter these valuse in the table shows the interpretation of the correlation coefficient between the interval from 0.20 to 0.399 which has a low relationship

Maintaining Customer Loyalty by Intensifying Service Quality and Customer Satisfaction in Commuter Line Jabodetabek

2019 ◽  
Vol 10 (9) ◽  
pp. 902-909
Author(s):  
Umbas Krisnanto ◽  
◽  
Conny Marpaung ◽  
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Customer Satisfaction ◽  
Service Quality ◽  
Hypothesis Testing ◽  
Customer Loyalty ◽  
T Test ◽  
Coefficient Of Determination ◽  
Simple Linear Regression ◽  
Significance Level

This study aims to determine and analyze the influence of Service Quality and Customer Satisfaction on Customer Loyalty in Jabodetabek Commuter Line. The sample of this study was 50 people. Methods of collecting data by distributing questionnaires. Data analysis using the analysis used is simple linear regression, t test and coefficient of determination. The results showed 1) Service Quality has a positive and significant effect on Customer Loyalty in Jabodetabek Commuter Line, with a significance level of 0.048; and supported by the results of hypothesis testing with a t-count value of 4.433 > t-table value of 1.95, with a significance of 0.048 or < 0.05; 2) Customer Satisfaction positive and significant effect on Customer Loyalty in Jabodetabek Commuter Line, with a level significance of 0,000; and supported by the results of hypothesis testing with a t-count value of 4,969 > t-table value of 1.95, with a significance of 0,000 or < 0.05, 3) Service quality and Customer Satisfaction have a positive and significant effect on Customer Loyalty in Jabodetabek Commuter Line, with a significance level of 0,000. This means that the hypothesis H0 is rejected and Ha is accepted so that it can be concluded that service quality and customer satisfaction together have a positive and significant effect on customer loyalty in Jabodetabek Commuter Line.

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