Epic research weekly commodity report 17 th to 21st apr 2017Epic Research
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Epic research weekly commodity report 17 th to 21st apr 2017Epic Research
Epic Research Limited counts as the top research providing firm all over India as well as other global markets.
Traders and investors can see our past performance and track-sheets which are easily available on our website. They also can avail the latest market updates and market trends by subscribing our daily or weekly newsletter.
Columbia University Baseball Analytics Case CompetitionTanner Crouch
On November 18th, our University of Florida team competed in the Columbia University Diamond Dollars Baseball Analytics Case Competition. We presented our picks for the three most valuable position players in Major League Baseball, as found by our future performance and contract models. We also discussed the potential risks of our predictions, as well as the impact of the upward-moving MLB strikezone on current top performers.
On November 18th, our University of Florida team competed in the Columbia University Diamond Dollars Baseball Analytics Case Competition. We presented our picks for the three most valuable position players in Major League Baseball, as found by our future performance and contract models. We also discussed the potential risks of our predictions, as well as the impact of the upward-moving MLB strikezone on current top performers.
2016 Diamond Dollars Case Competition - Columbia Univ.RJ Walsh
I am part of a sports analytics team at the University of Florida, and in November of 2016, we competed in Vince Gennaro’s Diamond Dollar Case Competition at Columbia University. The prompt was to find the three most valuable position player assets in baseball. Using R programming and Excel sheets, we were able to forecast future player production, predict future salary figures for these players, and incorporate risk into our analysis of each player to determine our top three.
Columbia University Baseball Analytics Case CompetitionTanner Crouch
On November 18th, our University of Florida team competed in the Columbia University Diamond Dollars Baseball Analytics Case Competition. We presented our picks for the three most valuable position players in Major League Baseball, as found by our future performance and contract models. We also discussed the potential risks of our predictions, as well as the impact of the upward-moving MLB strikezone on current top performers.
On November 18th, our University of Florida team competed in the Columbia University Diamond Dollars Baseball Analytics Case Competition. We presented our picks for the three most valuable position players in Major League Baseball, as found by our future performance and contract models. We also discussed the potential risks of our predictions, as well as the impact of the upward-moving MLB strikezone on current top performers.
2016 Diamond Dollars Case Competition - Columbia Univ.RJ Walsh
I am part of a sports analytics team at the University of Florida, and in November of 2016, we competed in Vince Gennaro’s Diamond Dollar Case Competition at Columbia University. The prompt was to find the three most valuable position player assets in baseball. Using R programming and Excel sheets, we were able to forecast future player production, predict future salary figures for these players, and incorporate risk into our analysis of each player to determine our top three.
3. Methodology
• Compute a new statistic “WAR+” to measure value to team
WAR+ = Projected WAR
(Salary/ Marginal Value)
Sum of annual WAR+ values for remaining years under contract
- Measure of added value over duration of contract
4. Aging Curves
• Compute Aging curves to breakdown three main components of WAR
–wRAA
–UZR (rSB and rPP for catchers)
–BsR
• Each aging curve broken down by standard deviation above/below the mean
– Account for deviation in
better/worse subsets
– 18 total aging curves
Highest Standard Deviation Group
3
6. Recalculating WAR
● Aging Curves extrapolated (1) wRAA (2) UZR (3) BsR through
2028
○ Based on 2016 original data
● League Adjustment, Positional Adjustment, Replacement Runs,
Runs Per Win kept constant
○ Not assuming position switch
7. Contract Projections
● Pre-Arbitration
○ Projections purely based on service time
● Arbitration
○ Arbitration model based on arbitration class and projected WAR totals
○ Assembled arbitration database on players from 2012-2016
● Contracts
○ Options assumed to be accepted given value
8. ● Marginal Value: Cost per year adjusted for inflation
○ Cost of win for 2017: $7,000,000
○ Inflation Percentage: 5%
Marginal Value
9. WAR / $
● “WAR+” to measure value to team over duration of contract
WAR+ = Projected WAR
(Salary / Marginal Value)
● Large contracts are more burdensome than lower WAR figures
10. Life of Contract WAR and $
• WAR through 2028
–Mike Trout (3rd)
–Large Contract + High Production
• Salary owed through duration of contract
–Giancarlo Stanton (1st)
–Miguel Cabrera (2nd)
–Joey Votto (3rd)
11. Risk Assessment
• Three Part Factor (Weighted on strength of the model)
–Injury Risk
–Age Risk
–Performance Risk
• Injury Risk
–DL days projection based on lag and two lags of annual DL days
• Age Risk
–Standard deviations of wRAA, UZR, BsR by age
• Performance Risk
–Standard deviations of annual WAR performance
–Rookies with 1 year of experience projected on average Yr 1 to 2 risk
12. Risk in Conjunction with Projections
• Applied Error Bars
–Upper and lower bounds of projection
• Acceptable level of risk varies by team situation
–Lower payroll teams cannot afford as much risk
• Lower bound of risk-associated projection used as final figures
–Worst possible likely outcome assumed