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: The Power of Analytics to Make Every Decision a Winner [ Larry E. Rosenberger, John Nash, Ann Graham] on domaine-solitude.com *FREE*. The Deciding Factor: The Power of Analytics to Make Every Decision a Winner. Larry E. Rosenberger, John Nash, Ann Graham (With). ISBN: .
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. In today's high-tech world, all companies are striving to create business value from digital data, but the real value in data comes from how it is used to make decisions. At the end of the day, what drives the results your company achieves is the millions of decisions made every day that are influenced by customer interactions and transactions.
In this groundbreaking book, Larry Rosenberger and John Nash draw on over fifty years of experience in helping companies automate, improve, and connect decisions. The authors explain how making better decisions through a combination of data, mathematics, and software can lead to a more customer-centric, cost-competitive, and creative organization.?? Highly accessible, The Deciding Factor helps demystify the math and information technology behind decision management for the business manager who may not be a mathematics or computer science wizard.
As practical as it is approachable, the book answers such questions as: How does a multinational consumer packaged-goods company build brand loyalty one person at a time? How does a consumer credit-card company process millions of transactions every second and keep fraud under control? How can a big-box retailer increase per-customer profitability? Rosenberger and Nash offer a much-needed resource for decision makers from the boardroom to the front line, a resource that provides guidance to create and unlock new sources of value from an organization's decisions.
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. John Nash is Fair Isaac's vice-president of corporate strategy, leading strategy development across the company's product portfolio. Nash has worked with Fortune companies in financial services, retail, consumer packaged goods, and high tech. Would you like to tell us about a lower price? If you are a seller for this product, would you like to suggest updates through seller support?
Learn more about Amazon Prime. Praise for The Deciding Factor "Both companies and governments have made some poor decisions recently, and almost all would benefit from more fact-based and analytical approaches. Davenport, author, Competing on Analytics, and President's Distinguished Professor of Information Technology and Management, Babson College "The secrets of the decision-making processes employed by the most successful corporations of the world are revealed in The Deciding Factor.
Read more Read less. Prime Book Box for Kids. From the Inside Flap The Deciding Factor In today's high-tech world, all companies are striving to create business value from digital data, but the real value in data comes from how it is used to make decisions. Jossey-Bass; 1 edition March 16, Language: Related Video Shorts 0 Upload your video. Share your thoughts with other customers. Write a customer review. There was a problem filtering reviews right now.
Please try again later. Kindle Edition Verified Purchase. Bought this book based on a quick look at a book store.
I get the feeling that the company "Fair and Isaac" has sponsored this book. John Nash is Fair Isaac's vice-president of corporate strategy, leading strategy development across the company's product portfolio. Nash has worked with Fortune companies in financial services, retail, consumer packaged goods, and high tech. Request permission to reuse content from this site. The Disciplines of Decision Leaders: The Future of Decision Management: Product not available for purchase. Description Praise for The Deciding Factor "Both companies and governments have made some poor decisions recently, and almost all would benefit from more fact-based and analytical approaches.
Davenport, author, Competing on Analytics, and President's Distinguished Professor of Information Technology and Management, Babson College "The secrets of the decision-making processes employed by the most successful corporations of the world are revealed in The Deciding Factor. The win probability model is built using the statistical modeling technique of logistic regression.
Given any game situation, the model takes the state of the game and generates a win probability describing the chances of winning for both teams. The time is normalized to equal 1 at the beginning of regulation and 0 at the end of regulation. The features in the model consist of nonlinear functions of score differential and time remaining in the game. The motivation behind using these nonlinear functions is the intuition that these variables combine nonlinearly to affect the probability of winning.
Many features of this nature were tried with the validation set guiding the choice of the final model via maximum likelihood. The model was then run on an independent test set.
On these test data with a win probability cutoff of 0. These values indicate that the logistic regression model provides a reasonable estimate of in-game win probability.
This paper presents the development of an end-of-game tactics metric ETM to inform in-game decision-making. Win probability for a team winning by three points without possession for two tactics. Rosenberger and Nash have put together a concise, well written and relatively easy to follow book to explain exactly how using analytics will enhance your business decisions. In this groundbreaking book, Larry Rosenberger and John Nash draw on over fifty years of experience in helping companies automate, improve, and connect decisions. The win probability of a trailing team can be used to find the time at which intentionally fouling becomes the optimal tactic given a score differential. Stage 3 Design Decision Architecture:
Figure 1 provides an illustration of how the logistic regression model maps to in-game win probability given different game states. The figure contains the win probability plots of a team with a given score differential and how the win probability changes within the last three minutes. For each time point, the win probability corresponds to a team starting possession at that moment with the circumstances shown in the figure.
This plot reveals several features of the win probability model. As the score differential gets more positive, the resulting increase in win probability gets smaller, which agrees with intuition as the win probability asymptotically approaches one. Smaller score differentials have extreme win probability behavior as the time remaining approaches zero. Win probabilities for different score differentials S and game time remaining for a team with possession and a point spread of zero. The win probability model serves as the foundation for ETM because, at the beginning of each possession, both teams have an initial win probability and a set of choices to make.
At a tactical level, these choices include whether to shoot a two-point or three-point field goal for the offensive team and whether to intentionally foul for the defensive team, which are the only decisions evaluated in this study. 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. 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. 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