Prior to every event, Under the Radar will cast the spotlight on an up-and-coming talent who shows the potential for growth in their division and isn’t getting enough attention as they head into battle.Name: Tywan Claxton Join DAZN and watch Bellator 212 and more than 100 fight nights a yearDespite kicking off this weekend’s doubleheader, this is without question a showcase opportunity for Claxton.He should win. He should look good doing it. And he should establish himself as someone to keep tabs on going forward. Record: 3-0 overall, 3-0 Bellator MMADivision: FeatherweightTeam: Warehouse Warriors (Cleveland, Ohio)Join DAZN and watch Bellator 212Tywan Claxton went viral with his first victory inside the Bellator MMA cage:The 26-year-old featherweight needed just a tick under 90 seconds to do away with Jonny Bonilla-Bowman, connecting with a beautiful flying knee from a dead stop that left his opponent catatonic on the canvas.Despite the impressive introduction, the former Division II All-American wrestler has had a hard time cracking the list of top upstart talents competing under the Bellator MMA banner, though not for a lack of trying.Since icing Bonilla-Bowman at Bellator 186, Claxton has added a second-round stoppage win over Jose Perez and a unanimous decision triumph over Chris Lencioni to push his record to a perfect 3-0. But competing in a division with elite prospects like AJ McKee, who kicks off Friday’s main card in Hawaii, and Aaron Pico, who is expected to return in January, makes grabbing a piece of the spotlight difficult. And being stationed in the opening bout of this weekend’s first event doesn’t help much either.But here’s the thing: unlike McKee and Pico, Claxton is nowhere close to being ready to challenge for the title and so bringing him along slowly like this is the right play.As much as I would like to see him get more than a couple weeks notice before jumping into the cage and facing someone with a little more to offer than the man he faces this week, Kaeo Meyer, this seems to be Bellator’s preferred approach with many of its inexperienced, but athletically established newcomers and it has paid dividends thus far.Based in Cleveland, the featherweight neophyte spent time training with Henri Hooft and the Blackzilians before that squad’s collapse, but has continued to work with grappling coach Neil Melanson and strength coach Jake Bonacci, both of which are major positives. While joining forces with a major camp — or at least completing his training camps with a more established unit — would certainly expedite his growth and development, there is something to be said for being comfortable where you train and Claxton seems settled with the Warehouse Warriors squad.Gaining experience and dealing with challenging situations are the keys for Claxton at this point, as he’s likely to be the superior athlete and heavy favorite heading into the cage in each of his next three or four outings. While it’s great to step into the cage and show out, there is no substitute for live rounds when you’re just getting started, even if it means focusing on individual skills and not necessarily playing to your strengths.With some of Bellator’s top up-and-comers starting to work their way into more prominent position in their respective divisions, new names will need to step up to fill the void and Claxton has the potential to be one of those fighters.How he performs this weekend and in his next three or four fights will go a long way to dictate how far “Air Claxton” will go in the Bellator MMA featherweight division.
Citation: Predictive algorithms are no better at telling the future than a crystal ball (2018, February 12) retrieved 18 July 2019 from https://phys.org/news/2018-02-algorithms-future-crystal-ball.html For example, a tool used in some parts of the United States to assess whether a person arrested for a crime would re-offend was found to unfairly discriminate against African Americans.They lead to self-fulfilling propheciesAnother flaw in the predictions thrown up by algorithmic analysis is their propensity to create self-fulfilling prophecies. Acting on algorithmic predictions, managers can create the conditions that ultimately realise those very predictions. For example, a company may use an algorithm to predict the performance of its recently-hired salespeople. Such an algorithm might draw on data from standardised tests completed during their onboarding process, reviews from previous employers, and demographics. This analysis can then be used to rank new salespeople and justify the allocation of more training resources to those believed to have greater performance potential. This is likely to produce the very results that the initial analysis predicted. The higher-ranked recruits will perform better than those ranked lower on the list because they have been given superior training opportunities.Calculating probabilities of future events is meaninglessSome practitioners recognise the flaws in the predictive capability of algorithmic systems, but they still see value in generating models that indicate probability. Rather than predicting the occurrence of future events or states, probabilistic models can indicate the degree of certainty that events or situations might occur in the future.However, here too it pays to be a little sceptical. When a model calculates that an event is likely to happen it does so as a percentage of 100% certainty. Any probabilistic prediction is only possible in relation to the possibility of complete certainty. But since complete certainty is impossible to predict, probabilistic models are of no real significance either. Algorithms don’t ‘predict’, they ‘extrapolate’So if they cannot predict organisational events with complete or even probable certainty, what can predictive algorithms do? To answer this, we must understand how they work. Once developed and inscribed with their base code, predictive algorithms need to be “trained” to hone their predictive power. This is done by feeding them with past organisational data. They then search for trends in the data and extrapolate rules that can be applied to future data. For example, workforce planning algorithms can identify employees who are likely to resign. They do this by analysing the personality and behavioural patterns of employees who have resigned in the past and cross-referencing the results with the profiles of existing employees to identify those with the highest matching scores. With each round of application, the algorithm is continually adjusted to correct ever-decreasing prediction errors.However, the term “prediction error” is misleading because these algorithms do not predict, but rather extrapolate. All that predictive algorithms can ever do is guess at what is going to happen based on what has already happened. The leap required to make actual predictions is not a matter of computing power, but rather of bending the laws of physics.Predictive models can’t anticipate changeBecause they are extrapolative, predictive models are rather good at identifying regularities, continuity and routine. However, the human brain is also designed to identify stable patterns. Competent managers should be well aware of their organisation’s operations, and capable of envisioning steady patterns over time. What managers find difficult to predict is change. Unfortunately, predictive models are also poor predictors of change. The more radical change is – different from existing patterns – the more poorly predicted it will be.To manage effectively and develop their knowledge of current and likely organisational events, managers need to learn to build and trust their instinctual awareness of emerging processes rather than rely on algorithmic promises that cannot be realised. The key to effective decision-making is not algorithmic calculations but intuition. This article was originally published on The Conversation. Read the original article. Provided by The Conversation Many analysts and professional practitioners believe that, with enough data, algorithms embedded in People Analytics (PA) applications can predict all aspects of employee behavior: from productivity, to engagement, to interactions and emotional states.Predictive analytics powered by algorithms are designed to help managers make decisions that favourably impact the bottom line. The global market for this technology is expected to grow from US$3.9 billion in 2016 to US$14.9 billion by 2023. Despite the promise, predictive algorithms are as mythical as the crystal ball of ancient times. Predictive models are based on flawed reasoningOne of the fundamental flaws of predictive algorithms is their reliance on “inductive reasoning”. This is when we draw conclusions based on our knowledge of a small sample, and assume that those conclusions apply across the board. For example, a manager might observe that all of her employees with an MBA are highly motivated. She therefore concludes that all workers with an MBA are highly motivated. This conclusion is flawed because it assumes that past patterns will remain consistent. This assumption itself can only be true because of our experience to date, which confirms this consistency. In other words, inductive reasoning can only be inductively justified: it works because it has worked before. Therefore, there is no logical reason to assume that the next person our company hires who has an MBA degree will be highly motivated. Assumptions like these can be coded into hiring algorithms, which, in this case, would assign a weighting to all job applicants with an MBA degree. But when inductive reasoning is baked into the code of hiring applications, it can lead to unfounded decisions, adversely impact on the bottom-line, and even discriminate against certain groups of people. Can math predict what you’ll do next? This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only. Explore further An increasing number of businesses invest in advanced technologies that can help them forecast the future of their workforce and gain a competitive advantage. The global market for predictive analytics is growing. Credit: Shutterstock read more