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What Makes The Formula One Champion. Regression Analysis
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What Makes The Formula One Champion. Regression Analysis

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The presentation is a summary of a half-year research. It shows how a careful allocation of funds possessed by team may influence its final position at the end of a season. The model predicted the ...

The presentation is a summary of a half-year research. It shows how a careful allocation of funds possessed by team may influence its final position at the end of a season. The model predicted the whole podium of 2011 F1 season.

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What Makes The Formula One Champion. Regression Analysis What Makes The Formula One Champion. Regression Analysis Presentation Transcript

  • What Makes The Formula One Champion? Regression AnalysisWojciech Latocha
  • WHAT IS FORMULA ONE?• the highest class of single-seater auto racing• the season is compounded of a series of races, known as Grand Prixes• races are held on purpose-built circuits and public roads• results of each race are added to define two annual World Champions: • individual • constructor
  • THE IMPORTANCE OF THE F1 ANALYSIS• Social factors: • Worldwide popularity • each F1 Grand Prix attracts a global audience of 600 million people • 2001 F1 season: 54 billion TV viewers, 200 countries• Economic factors: • Perfect example for Snob Effect and Veblen Good concept • Formula One revenues are equal to GDP of Mongolia or Kyrgyzstan and almost two times higher than GDP of Faroe Islands
  • LITERATURE REVIEW• Numerous general nature book publications and articles: • Explaining rules • Presenting history • Describing former World Champions • Liu Xiao: Formula One’s Financial Crisis, 2009• A few scientific resources: • Eichenberger and Stadelmann, Who Is The Best Formula 1 Driver? An Economic Approach to Evaluating Talent, 2009 • Mastromarco and Runkel, Rule changes and competitive balance in Formula One motor racing, 2009
  • DATA• The official website of the Formula One• Official websites of Formula One teams• Official websites of drivers• Formula Money
  • METHODOLOGY• Regression Analysis• Ordinary Least Squares Methodology• STATA software
  • Regression Model
  • SIMPLE DATA SERIES• Was the individual world champion racing for the team? – a dummy variable• Number of years that a main engineer spent with a team
  • RATIOS• Percentage spread between drivers points• First driver salary / First driver podiums• First driver salary / First driver positions awarded with points (excluding podiums)• Second driver salary / Second driver podiums• Second driver salary / Second driver positions awarded with points (excluding podiums)• Teams testing laps / The most active testers laps• Pit-stop spread
  • REASONS OF THE DISTINCTION• Changes in regulations• Forecasting disability of some simple series (no ability of their values control)• Driver’s transfers• Number of competing team’s changes
  • SUMMARY OF STATISTICS Standard Variable Mean Deviationfinal position 6.018182 3.223128was the individual world champion racing for the team? 0.090909 0.290129percentage spread between drivers points 0.692449 0.341747number of years main engineer spent with the team 2.927273 2.417946teams testing laps to the most active testers laps 0.69628 0.270442pit stop spread 0.187378 0.1931951st salary/podiums 4.589197 6.1905811st salary/points 2.642526 3.2185022nd salary/podiums 3.351749 5.2923862nd salary/points 1.857905 2.539029
  • RESULTS OF THE REGRESSION final position Coefficient Standard Error t P>twas the individual world champion racing for the team? -1.86251 1.232371 -1.51 0.138percentage spread between drivers points 2.287651 .9805094 2.33 0.024number of years main engineer spent with the team -0.27952 .138966 2.01 0.05teams testing laps to the most active testers laps -1.88072 1.584256 -1.19 0.241pit stop spread 2.643283 1.948494 1.36 0.1821st salary/podiums -0.07556 .0665366 -1.14 0.2621st salary/points -0.1668 .1104518 -1.51 0.1382nd salary/podiums 0.195983 .0838879 2.34 0.0242nd salary/points -0.43651 .1611728 -2.71 0.01constant 7.177511 1.498906 4.79 0
  • DISCUSSION• 7.18 - an initial position of any team competing in The Formula One• 1.86 - a position improvement due to employing a former individual World Champion• 0.23 - a result depreciation caused by each 10% of a difference between points collected by each of two drivers• 0.28 - a position increase through prolonging team’s cooperation with its main engineer• 0.44 - a position improvement due to each $1 million paid to the second driver for scoring a position points-awarded• 0.2 - a position drop caused by each $1 million paid to the second driver for scoring a podium
  • FORECAST FOR 2011 Virgin-Cosworth HRT-Cosworth Lotus-Renault Williams-Cosworth STR-Ferrari Sauber-FerrariForce India-Mercedes Renault Mercedes Ferrari McLaren-Mercedes RBR-Renault 0 2 4 6 8 10 12 actual final position predicted position
  • CONCLUSION• The model was able to predict an exact final position of one out of twelve teams• Numerical values returned by the equation indicated four out of twelve positions in the final standing, including: • The World Champion • All three teams on the podium• The forecast indicated a fierce battle for a few positions – what was proved on the track• The model underestimated a result of the first seven teams and overestimated the result of the others
  • Thank you foryour attention!