The Deciding Factor: The Power of Analytics to Make Every Decision a Winner

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Would you like to change to the United States site? This book provides clear methods and extensive examples for organizations that want to make better, faster, and more consistent decisions. Both corporate decision makers as well as analysts will gain invaluable insights from this treasure trove of case studies and expert guidelines. The Deciding Factor will help you understand if you have this opportunity, and how you might seize it.

If you're prepared to be serious, The Deciding Factor offers the insider's insights that matter when managing innovation risk. Larry Rosenberger is widely recognized as an innovator in decision technology, particularly in consumer lending. He was named Fair Isaac's first analytic research fellow in , following more than 33 years of service to the company. In this capacity, he continues to pursue research projects that advance Fair Isaac's analytic science. For example, in Stage 1 , the key questions for the organization to ask about the Decision Strategy are outlined: What are the Possibilities?

What are the important opportunities in alignment with company strategy or problems to be solved? What are the most important decisions related to these opportunities?

Straight talk about big data | McKinsey

How should opportunities be pursued based on priorities? I believe the case is made that the continuing productivity improvements from our IT investments will come from using Analytics to help make rational, data driven decisions to better serve customers and clients. The Deciding Factor provides a clear understanding and methodology to move your company to the next level of productivity. One person found this helpful 2 people found this helpful. This is a great book if you have a need to sell executive leadership on the usefulness of extracting additional value out of organizational data.

I bought this book with great anticipation as I have been on the search for books that describe how to leverage data analytics within business I kept waiting for the punchline. There were many references as to how decision analytics can drive efficiency gains and revenue, yet no noticeable mention of how to go about doing it.

Evaluating NBA end-of-game decision-making

It read like a subtle sales pitch as to why you should hire Fair Issac to help you get there. There was barely enough technical jargon included to even go start researching descriptive and predictive analytics models. I found myself going to wikipedia and reading about this subject, then going back to the book, waiting.. In protest, I refused to read the last 5 pages. There's value in the book for those business leaders that have absolutely no knowledge of the topic, which is why I gave it 3 stars. Just don't go thinking that it provides any insight as to how to start extracting that value.

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The Power to Decide

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ComiXology Thousands of Digital Comics. East Dane Designer Men's Fashion. Shopbop Designer Fashion Brands. Withoutabox Submit to Film Festivals. The timing of these decisions also factor into the decision-making process. The possibility of a turnover or free throws on a shooting foul exists, but these are not explicit choices made by teams. The Chapman-Kolmogorov equations allow for the calculation of the win probability of a team after a given decision is made.

Data and Decision Making

In this context, the equation states that the probability of winning a game after making a decision k is the sum of the probability of all of the possible outcomes of that decision multiplied by the probability of winning after those outcomes. The win probability term of Eq. The P j term comes from evaluating how all of the different outcomes of a decision can result in score differential j.

For example, for the score differential to remain the same after a decision to shoot a two-point field goal, this can occur if the team misses the field goal attempt, turns the ball over, or misses both free throw attempts followinga shooting foul for simplicity, this model ignores getting fouled during a three-point field goal attempt. The probabilities of all of these outcomes come from team statistics, including two-point field goal percentage, three-point field goal percentage, turnover percentage, free throw percentage, rebound percentage, and foul percentage.

These statistics vary by team and this study uses team statistics rather than aggregated league averages. By definition, ETM represents the win probability a team sacrifices by not making the optimal decision. Therefore, the goal of a team would be to minimize its ETM. The value of ETM stems from the quantifying of the effects of in-game decisions at the end of close games, regardless of the outcome of the decision. For aggregate ETM results, the above models used play-by-play data from the NBA season from basketball-reference.

Team fouls per possession come from teamrankings. The models only used data from games that ended with a score differential within five points so as to focus on games where the outcome was certainly in question in the last three minutes. In addition, a shooting weight for field goal percentage is applied to team shooting percentages to account for the effect of the shot clock on shooting percentage.

This was found by fitting a quadratic function to team shooting percentage by shot clock time from nba.

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This shooting weight is set to one with fewer than 24 seconds remaining when the shot clock is off. The results include games ending with a score differential of five points or fewer and periods that lead to an additional overtime period. The results consider the final seconds of such periods. The line in Fig. The correlation coefficient of this relationship is The variability exhibited in Fig.

Winning percentage in such games also depends on execution in many facets of the game on the offensive and defensive sides of the ball. These other explanatory variables include the win probability of a team at the start of the remaining three minutes, the difference in field goals made between teams, and the difference in fouls committed between teams. Winning percentage in close games versus mean ETM difference for NBA teams during the season overlaid with the least-squares regression line.

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The histogram shows an albeit noisy trend of the team with a lower ETM enjoying a higher winning percentage. The source of the noise is almost certainly multidimensional, with possible sources being few games of a certain ETM difference and performance variables, as discussed above. Considering the results presented, ETM difference has a significant effect on winning close games, despite having no reliance on the outcome of aparticular play.

Histogram showing the results of close games in the NBA season. The general trend shows that the team with a lower ETM can expect a higher winning percentage. The tactic has been heavily debated, but analyses and studies on the topic tend to be heuristic in nature Annis, While these studies provide value, statistical modeling efforts like ETM can provide more insight.

Using ETM and win probability to examine this tactic is quite straightforward.


  • Evaluating NBA end-of-game decision-making - IOS Press;
  • Data and Decision Making - MIT Technology Review.
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Consider a team on defense leading by three points. This team has the option to intentionally foul or play conventional defense. Win probability for a team winning by three points without possession for two tactics. The win probability of the intentional foul tactic exceeds the win probability of playing conventional defense beginning at nine seconds remaining. Notice how the defensive win probability changes throughout the last 36 seconds of the game in Fig.

With 36 seconds remaining, the conventional defensive tactic win probability is 3.

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