In this note, I introduce Estimated Performance Rating (PR$^e$), a novel system for evaluating player performance in sports and games. PR$^e$ addresses a key limitation of the Tournament Performance Rating (TPR) system, which is undefined for zero or perfect scores in a series of games. PR$^e$ is defined as the rating that solves an optimization problem related to scoring probability, making it applicable for any performance level. The main theorem establishes that the PR$^e$ of a player is equivalent to the TPR whenever the latter is defined. I then apply this system to historically significant win-streaks in association football, tennis, and chess. Beyond sports, PR$^e$ has broad applicability in domains where Elo ratings are used, from college rankings to the evaluation of large language models.

The Summary Plot above shows that high values (red points) of "PercentSalaryHike" are associated with positive SHAP values and low values (blue points) of "PercentSalaryHike" are, in general, associated with negative SHAP values.

Artificial intelligence has disrupted every area of our lives -- from the curated shopping experiences we've come to expect from companies like Amazon and Alibaba to the personalized recommendations that channels like YouTube and Netflix use to market their latest content. But, when it comes to the workplace, in many ways, AI is still in its infancy. This is particularly true when we consider the ways it is beginning to change talent management. To use a familiar analogy: AI at work is in the dial-up mode. The 5G WiFi phase has yet to arrive, but we have no doubt that it will.

With the industry trend of shifting from a traditional hierarchical approach to flatter management structure, crowdsourced performance assessment gained mainstream popularity. One fundamental challenge of crowdsourced performance assessment is the risks that personal interest can introduce distortions of facts, especially when the system is used to determine merit pay or promotion. In this paper, we developed a method to identify bias and strategic behavior in crowdsourced performance assessment, using a rich dataset collected from a professional service firm in China. We find a pattern of "discriminatory generosity" on the part of peer evaluation, where raters downgrade their peer coworkers who have passed objective promotion requirements while overrating their peer coworkers who have not yet passed. This introduces two types of biases: the first aimed against more competent competitors, and the other favoring less eligible peers which can serve as a mask of the first bias. This paper also aims to bring angles of fairness-aware data mining to talent and management computing. Historical decision records, such as performance ratings, often contain subjective judgment which is prone to bias and strategic behavior. For practitioners of predictive talent analytics, it is important to investigate potential bias and strategic behavior underlying historical decision records.

The problem of rating the performance of soccer players is attracting the interest of many companies, websites, and the scientific community, thanks to the availability of massive data capturing all the events generated during a game (e.g., tackles, passes, shots, etc.). Existing approaches fail to fully exploit the richness of the available data and lack of a proper validation. In this paper, we design and implement {\sf PlayeRank}, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We validate the framework through an experimental analysis advised by soccer experts, based on a massive dataset of millions of events pertaining four seasons of the five prominent European leagues. Experiments show that {\sf PlayeRank} is robust in agreeing with the experts' evaluation of players, significantly improving the state of the art. We also explore an application of PlayeRank --- i.e. searching players --- by introducing a special form of spatial query on the soccer field. This shows its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.

Carnegie-Mellon University's Hitech chess computer scored 5-1 in the National Open Chess Championships held in Chicago March 18-20. The Championship Section in which Hitech competed, had 380 entries. The Championship Section in which Hitech competed, had 380 entries. There was a six-way tie for first with 5.5 points between: International Grandmaster Mikhail Tal (a former world champion), International Grandmaster Sergey Kudrin, FIDE Master Michael Brooks, International Master James Rizzitano, International Master Calvin Blocker, and International Grandmaster Leonid Shamkovich. Tied for seventh with 5 points were: National Master Hitech, International Grandmaster Maxim Dlugy, International Grandmaster Walter Browne, International Grandmaster Arthur Bisguier, and nine others.

There was a six-way tie were losses to International Master's, for first with 5.5 points between: and there were two draws against International Grandmaster Mikhail players rated over 2500, (one an International Tal (a former world champion), International Master). Grandmaster Sergey Kudrin, From previous tournaments rated FIDE Master Michael Brooks, International by FIDE, the international chess federation, Master James Rizzitano, International Hitech has achieved a performance Master Calvin Blocker, and worthy of a FIDE rating. However, International Grandmaster Leonid at present FIDE is declining to Shamkovich. Hitech has met every points were: National Master Hitech, qualification but one for achieving a International Grandmaster Maxim rating--it is not a human. If Hitech Dlugy, International Grandmaster were eligible for a rating, its FIDE rating Walter Browne, International Grandmaster would be 2350, which would qualify Arthur Bisguier, and nine others.