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In a new study published in the journal Social Neuroscience, researchers employed machine learning techniques to predict individual differences in narcissistic personality traits using distinct structural brain features. The study represents the first-ever attempt to harness machine learning for deciphering the neural underpinnings of narcissism.

Narcissistic traits encompass characteristics such as grandiosity, a constant need for admiration, a lack of empathy, entitlement, manipulative behavior, envy, arrogance, fragile self-esteem, and difficulties in maintaining healthy relationships. These traits reflect a self-centered and often arrogant perspective, where individuals may believe they are superior to others and expect special treatment.

When narcissistic traits are severe and persistent, they may lead to a diagnosis of narcissistic personality disorder, a complex clinical construct often comorbid with other psychological disorders such as borderline personality, substance abuse, antisocial tendencies, and anxiety. However, diagnosing narcissistic personality disorder can be challenging, as it relies on self-reported and observed behaviors, thoughts, and feelings. This is because there are no clear biological markers for the disorder, making it difficult to objectively assess the disorder.

The researchers sought to develop predictive models that could estimate an individuals narcissistic traits based on their brain structure and personality features. This has practical implications for psychology and clinical assessments. Predictive models could potentially help identify individuals at risk of developing narcissistic traits or assist in the assessment and treatment of personality disorders.

In our Lab, the Clinical and Affective Neuroscience Lab, we are particularly interested in understanding the neural fingerprint of personality. Especially personality disorders. We all have a personality that ranges from normal to abnormal traits and we believe it is of fundamental importance understanding it, explained study author Alessandro Grecucci, a professor of affective neuroscience and neurotechnology at the University of Trento.

The researchers conducted a study using data from the MPI-Leipzig Mind Brain-Body dataset, which included structural MRI and questionnaire data from 135 healthy participants. Eligibility criteria included good health, no medication, and no history of substance abuse or neurological diseases. The participants demographic and behavioral data were recorded.

Using a machine learning technique called Kernel Ridge Regression, the researchers found that specific brain regions were linked to narcissistic traits, including the orbitofrontal cortex, Rolandic operculum, angular gyrus, rectus, and Heschls gyrus. These regions are associated with emotion processing, social cognitive processing, and auditory perception.

The findings provide evidence that even such an intimate thing such as personality, the inner core of who we are, can be scientifically studied and predicted from our brain, Grecucci told PsyPost. In our lab, we are trying to develop neuro-predictive models of personality and other affective relevant dimensions. One day, these studies may help clinicians to characterize eventual difficulties before they turn into a full disorder.

Furthermore, the researchers constructed a predictive model to determine an individuals narcissistic traits based on specific subscales from the NEO Personality Inventory-Revised, Short Dark Triad questionnaire, and the Personality Styles and States Inventory.

Individuals with higher levels of openness, characterized by a willingness to explore new experiences and ideas, were more likely to exhibit narcissistic traits. Lower levels of agreeableness, which involve being less cooperative, sympathetic, and considerate of others, were associated with narcissistic traits. Higher levels of conscientiousness, indicating self-discipline, organization, and goal-oriented behavior, were linked to narcissistic traits.

Additionally, the study found that abnormal personality traits, including Borderline, Antisocial, Addicted, Negativistic, and Insecure traits, were related to narcissistic traits. Machiavellianism, characterized by manipulative and deceitful behavior, also predicted narcissistic traits. This suggests that individuals with narcissistic traits may exhibit a combination of personality traits, some of which are outside the normal range.

In this and other studies, we are observing an emerging coherent pattern in different personality disorders, Grecucci said. Regions belonging to the same cortical-subcortical networks are at a forefront. This may lead to the development of a common personality network behind specific personality traits.

The study provides new insights into the neural underpinnings of narcissism. But as with all research, it includes some limitations. Firstly, the analysis focused solely on gray matter features, neglecting potential insights that could be gained from exploring white matter features or functional brain activity. Future research may benefit from a more comprehensive examination of various brain aspects. Secondly, while the study included a relatively larger sample size compared to previous research, it acknowledges the potential for even larger sample sizes to enhance brain-wide association analyses.

The researchers also believe that clinical personality models offer more robust and predictive insights into personality traits than non-clinical models.

Personality is a complex thing, and no one knows which is the best model of personality we should use to study this topic at a brain level, Grecucci explained. Contrary to the vast majority of studies that are using normal personality models (such as the Big Five), we are trying to make a claim that personalities can be better captured using clinical models such as the DSM-5 personality disorder axis. The clinical personalities offer such a strong characterization of what different personalities are that in my opinion they can be more predictive than other non-clinical models. In the end, personality disorders are just exaggerated personality traits that we all have.

The study, Predicting narcissistic personality traits from brain and psychological features: A supervised machine learning approach, Khanitin Jornkokgoud, Teresa Baggio, Md Faysal, Richard Bakiaj, Peera Wongupparaj, Remo Job, and Alessandro Grecucci.

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Scientists use machine learning to predict narcissistic traits based on neural and psychological features - PsyPost

Summary: Researchers have utilized advanced machine learning techniques to unveil the neural structure linked to narcissism, overcoming previous study limitations.

Employing Kernel Ridge Regression and Support Vector Regression, they predicted narcissistic personality traits based on brain organization and other personality aspects. A specific brain circuit, including regions like the lateral and middle frontal gyri, played a predictive role.

Furthermore, a combination of both conventional and abnormal personality traits could forecast narcissism.

Key Facts:

Source: Neuroscience News

Narcissisma term that often garners interest in both academic circles and daily conversations.

Often associated with pathological conditions, the neurological underpinnings of narcissism have remained a mystery. But recent advances in machine learning are shining a new light on this old enigma.

Past attempts to map the neural routes of narcissism have often fallen prey to inconsistent findings. Many of these inconsistencies were attributed to limitations such as low participant numbers or reliance on traditional univariate methods. These approaches were limiting the depth of insight possible into the intriguing world of narcissistic traits.

Determined to break past these barriers, a recent study employed cutting-edge machine learning techniques: Kernel Ridge Regression and Support Vector Regression.

These tools have the capability to discern and predict patterns in vast datasets, making them apt for an investigation into the intricate neural web of narcissism.

The aim was straightforward but ambitious: build a predictive model for narcissistic traits, relying on both neural structures and an array of personality features.

The results were both surprising and enlightening.

A specific brain circuit emerged as a powerful predictor of narcissistic personality traits. This circuit incorporates regions such as the lateral and middle frontal gyri, angular gyrus, Rolandic operculum, and Heschls gyrus.

The statistical significance (p<0.003) of this finding underscores its potential implications for both neuroscience and psychology.

But the revelations didnt stop at neural structures. The research unearthed a compelling blend of normal (e.g., openness, agreeableness, conscientiousness) and abnormal (e.g., borderline, antisocial, insecure, addicted, negativistic, Machiavellianism) personality traits that could forecast narcissism.

This multi-dimensional approach, combining neural with psychological markers, has opened up a more holistic understanding of narcissistic traits.

This study stands as the first of its kind to deploy a supervised machine learning approach in the pursuit of decoding narcissism. It hints at a future where personality traits could be derived, not just from observable behaviors, but from a mix of neural and psychological features.

While these findings are a monumental step, they also pave the way for further inquiry. How might these insights transform therapeutic interventions? Could they enhance diagnostic precision? The confluence of neuroscience and machine learning promises not just answers, but a richer understanding of the human psyche.

This multi-faceted exploration of narcissism exemplifies how modern tools can rejuvenate classical investigations. As we continue to harness the combined power of neuroscience and machine learning, the horizons of personality research are bound to expand exponentially.

Author: Neuroscience News Communications Source: Neuroscience News Contact: Neuroscience News Communications Neuroscience News Image: The image is credited to Neuroscience News

Original Research: Closed access. Predicting narcissistic personality traits from brain and psychological features: A supervised machine learning approach by Alessandro Grecucci et al. Social Neuroscience

Abstract

Predicting narcissistic personality traits from brain and psychological features: A supervised machine learning approach

Narcissism is a multifaceted construct often linked to pathological conditions whose neural correlates are still poorly understood.

Previous studies have reported inconsistent findings related to the neural underpinnings of narcissism, probably due to methodological limitations such as the low number of participants or the use of mass univariate methods.

The present study aimed to overcome the previous methodological limitations and to build a predictive model of narcissistic traits based on neural and psychological features.

In this respect, two machine learning-based methods (Kernel Ridge Regression and Support Vector Regression) were used to predict narcissistic traits from brain structural organization and from other relevant normal and abnormal personality features.

Results showed that a circuit including the lateral and middle frontal gyri, the angular gyrus, Rolandic operculum, and Heschls gyrus successfully predicted narcissistic personality traits (p<0.003).

Moreover, narcissistic traits were predicted by normal (openness, agreeableness, conscientiousness) and abnormal (borderline, antisocial, insecure, addicted, negativistic, machiavellianism) personality traits.

This study is the first to predict narcissistic personality traits via a supervised machine learning approach. As such, these results may expand the possibility of deriving personality traits from neural and psychological features.

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Machine Learning Uncovers Neural Pathways of Narcissistic Traits - Neuroscience News

The number of digital transactions has skyrocketed. As consumers continue to spend and interact online, they have growing expectations for security and identity verification. As fraudsters become savvier and more opportunistic, theres an increased need for businesses to protect customers from fraud while still providing a seamless online experience.

At the same time, businesses have the ability to access more insights and data than ever before, but may not be leveraging the most effective technology solutions to accurately identify and authenticate consumers online.

Uncertain economic conditions and what feels like a barrage of new scams has made consumers and businesses more concerned about online fraud.

Experians 2023 U.S. Identity and Fraud Report found that over half of consumers feel like they are more of a fraud target than they were just one year ago. In addition, half of businesses report a high level of concern about fraud risk.

The report found that people worry most about identity theft (64%), stolen credit card information (61%) and online privacy (60%). On the other hand, businesses are concerned about authorized push payments fraud (40%) and transactional payment fraud (34%). Additionally, nearly 70% of businesses said that fraud losses have increased in recent years and most businesses reported that they plan to increase their fraud management budgets by at least 8% to as much as 19%.

Despite their plans to increase their fraud prevention budgets, data shows that businesses may not be completely aligned with consumer expectations.

For example, 85% of people report physical biometrics, such as facial recognition and fingerprints, as the authentication method that makes them feel most secure. However, that identity authentication method is currently used by just a third of businesses to detect and protect against fraud, showing there is still a disconnect between consumer preferences and what businesses are offering.

Finally, consumers not only stress the importance of better security, but they expect their online experiences to be frictionless. This is evident in the data while 51% considered abandoning a new account opening because of a negative experience, 37% said a bad experience caused them to take their business elsewhere. Its crucial for businesses to implement fraud solutions that are capable of properly verifying real customers while identifying and treating fraud and providing a positive experience.

Businesses understand the need to incorporate machine learning into their anti-fraud strategies.

The main benefits of incorporating machine learning into fraud management is that it can:

A multilayered approach to fraud that leverages data, machine learning and advanced analytics is crucial for businesses trying to stay ahead of fraud trends. Machine learning modernizes identification and fraud prevention, allowing businesses to fight new and old forms of fraud as they occur while providing their customers with a seamless, positive experience.

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Modernizing fraud prevention with machine learning - Help Net Security

Japans leading developer of new AI technologies Fujitsu accelerates its commitment to open source innovation with new projects hosted in LF AI & Data Fujitsu Limited, The Linux Foundation

Tokyo, San Francisco, September 15, 2023

Fujitsu Limited and the Linux Foundation today marked the official launch of Fujitsus automated machine learning and AI fairness technologies as open source software (OSS) ahead of Open Source Summit Europe 2023, running in Bilbao, Spain, from September 19-21, 2023. The two projects will offer users access to software that automatically generates code for new machine learning models, as well as a technology that addresses latent biases in training data.

The Linux Foundation approved the incubation of two new projects, "SapientML" and "Intersectional Fairness" on August 24 to encourage developers worldwide to further experiment and innovate with AI and machine learning technologies, with plans to host future activities like hackathons to engage and build a community to promote open source AI.

With these projects, Fujitsu and the Linux Foundation aim to further democratize AI to realize a world in which developers everywhere can easily and securely use the latest technologies on open platforms to create new applications and find innovative solutions to challenges facing business and society.

Offering AI technologies as OSS to developers worldwide opens up new opportunities for innovation across various industries by lowering the barrier of entry. We are excited to work together with Linux Foundation to contribute to the further advancement and spread of AI by launching Fujitsus AI technologies for the Linux Foundations open source projects SapientML and Intersectional Fairness.

We anticipate that offering Fujitsu's automated machine learning and AI fairness technologies as OSS will greatly contribute to the advancement and diffusion of AI. The Linux Foundation welcomes these two projects and looks forward to building the future of AI together.

Fujitsu holds the largest number of AI-related inventions in Japan, with 970 patents from 2014 to October 2022, and in April 2023, launched Fujitsu Kozuchi (code name) - Fujitsu AI Platform. Kozuchi enables users to rapidly and securely test advanced AI technologies and has offered a wide range of customers and partners access to some of Fujitsus most advanced AI technologies.

AI represents one of the most rapidly developing technologies of our time, contributing to the solution of various societal and industrial issues. However, this ongoing development requires advanced expertise in the development and operation of AI technologies, and concerns about the fairness of AI technology keep increasing. To enable further spread and enhancement of AI, a platform to openly share AI technologies with engineers worldwide represents an important prerequisite.

As a non-profit technology consortium, the Linux Foundation approves approximately a dozen selected technologies as OSS projects annually for the development of open technologies. Fujitsu is providing automated machine learning and AI fairness technologies as OSS via Linux Foundation, enabling developers around the world to access and widely use Fujitsu's technology at the source code level to accelerate technological advancement and the development of new applications, while also addressing concerns around fairness and transparency that remain a critical priority in the field of AI ethics.

Fujitsu has been further providing its technologies for automated machine learning and AI fairness as Fujitsu AutoML and Fujitsu AI Ethics for Fairness, together with various AI technologies and GUIs via the Fujitsu Kozuchi (code name) - Fujitsu AI Platform. Moving forward, Fujitsu will offer technology updates for each project on its AI platform.

Fujitsus purpose is to make the world more sustainable by building trust in society through innovation. As the digital transformation partner of choice for customers in over 100 countries, our 124,000 employees work to resolve some of the greatest challenges facing humanity. Our range of services and solutions draw on five key technologies: Computing, Networks, AI, Data & Security, and Converging Technologies, which we bring together to deliver sustainability transformation. Fujitsu Limited (TSE:6702) reported consolidated revenues of 3.7 trillion yen (US$28 billion) for the fiscal year ended March 31, 2023 and remains the top digital services company in Japan by market share. Find out more: http://www.fujitsu.com.

The Linux Foundation is the worlds leading home for collaboration on open source software, hardware, standards, and data. Linux Foundation projects are critical to the worlds infrastructure including Linux, Kubernetes, Node.js, ONAP, PyTorch, RISC-V, SPDX, OpenChain, and more. The Linux Foundation focuses on leveraging best practices and addressing the needs of contributors, users, and solution providers to create sustainable models for open collaboration. For more information, please visit us at linuxfoundation.org. The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see its trademark usage page: http://www.linuxfoundation.org/trademark-usage. Linux is a registered trademark of Linus Torvalds.

Fujitsu Limited Public and Investor Relations Division Inquiries

All company or product names mentioned herein are trademarks or registered trademarks of their respective owners. Information provided in this press release is accurate at time of publication and is subject to change without advance notice.

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Fujitsu and the Linux Foundation launch Fujitsu's automated ... - Fujitsu

Baseline Scouting

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Our Startups series looks at companies and founders who are innovating in the fields of athlete performance, fan engagement, team/league operations and other high-impact areas in sports. If youd like to be considered for this series, tell us about your mission.

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Worlds shortest elevator pitch: Baseline Scouting is a next-generation scouting offering that combines the eye test with machine learning.

Company: Baseline Scouting

Location: New York, N.Y.

Year founded: 2021

Website/App: https://baselinescouting.com/

Funding round to date: We are self-funded/pre-seed.

Who are your investors? We have no investors yet, so we dont have any (funding) raise. We have roughly $10,000.

Are you looking for more investment? Yes.

Tell us about yourself, founder & CEO Anthony Herbert: I was born and raised in Queens, N.Y. I grew up a big basketball and track and field guy. Basketball was always something I had a passion for. Im built more for track long term, but never fell out of love with basketball. Growing up, I would always play video games, team build, draft and develop rosters and things like that. Once I finished running track in college, I still wanted to give back and help people learn things I didnt know growing up so they could develop faster. For me, its always been about development and sports aspects of it. The other half is IT. Growing up, my family joked I was always good with computers and I was the familys computers person when I was two. Once I got into high school I had a concentration in internetworking, so making networks work, computer networking, things like that. It slowly shifted to cybersecurity. When I got to college, those were my two concentrations: networking and cybersecurity. I realized the future was heading in that security direction. I went down the path, helped to found an information security club at the school and thats where my career took off. I learned about machine learning and behavioral analytics while working at Securonix, the leading company for SIEM and user-entity behavior analytics and data indexing and search. Taking both of those things, I was sitting in the living room one day and thinking of what I can do with these things. I have this passion for basketball and sports development and I have all this knowledge. I asked myself: What sets LeBron apart? What sets Magic Johnson apart from every other 6-7, 6-10 person that can jump? They do the right thing and they do it 9.5 times out of 10. When you think about doing the right thing and what that entails, thats behavioral. Thats when things started to click because I was like, I know how to look at it and analyze that it. That really spearheaded the company.

Who are your co-founders/partners? My co-founder is Erick Garcia. He is a best friend from middle school and very basketball oriented. He loves a lot of sports, but baseball and basketball are his two biggest sports loves. Hes not from a tech background, but is very much into New York sports culture. In working on this, we realized he has the knack for scouting. He takes so much initiative when it comes to learning what to scout. He is crucial in helping develop what we look for, how we develop our proprietary statistics and is our chief scout and co-founder. We are partnering with Zinn Sports Groups Sandy Zinn. Hes instrumental in consulting and advising us, basically saying, I believe in what you have, but you have to put it in a way that you can demonstrate what you are doing. We had a concept and semi-idea of what our product would be, and now we have a full-fledged product, social media presence and were doing more things. Thats where he stands out, his industry knowledge and connections and ability to level with us about what we need to do.

How does your platform work? The platforms name is War Room. It is the portal in which we give teams, agents and other end users the ability to see our scouting results but also have access to our data. Thats the two biggest things to say when were selling it to them. Not only are we selling our scouting metrics and results and these player profiles, but were also giving you all this data you can use to say, Hey, this is a different way of how they are looking at things but in the same vein, they are looking at it in an inclusive way for people who just believe eyes. I need to look at video, I can tell you whats good, but then theres a newer wave of, No, I need analytics. We need these advanced metrics and things of that nature. Its subscription-based, giving them access to the platform that meshes everything together for a comprehensive and next-generational picture of scouting. Were currently doing basketball but the plan is to expand pretty soon into other sports. Basketball is our bread and butter and root.

What problem is your company solving? Were solving a few problems. The biggest one is that sports scouting, as a whole, has this level of uncertainty that from time to time rears its head. It goes beyond taking someone at the top of the draft that doesnt pan out. Its also missing out at the end of the draft. Were about being able to quickly and better than everyone else identify where the value is at all levels of the draft, but also making sure teams are getting the biggest return on their value by doing so. The average career for most athletes in most sports lasts four to five years. You have to find that value to have sustained success. Last but not least, these last five years its been really uncertain things all over the place with the pandemic, inflation, things of that nature, so how do you make it so it stands the test of time? Maybe you dont have the resources to get to somewhere in person or to scour tape for hours. We have a system and company resources to do that and aid a team.

What does your product cost and who is your target customer? Our target customers are teams. We also are looking for agents, so anyone who has a value in regards to scouting and these amateur or international pros that havent been to the league yet. The cost for a subscription of one year is $1.5 million and $2 million for two years.

How are you marketing your product? Were doing a few things. Since were going business to business, its not just the traditional path of going on social media, trying to draw as many people as possible. We are still using social media as a visual store to say, These are various samples, bits and pieces of what we are doing, to show you this is what we look at. These are some of our results. Feeding these different tidbits to draw interest from those teams and at least have them asking questions like, How did you arrive at this conclusion, while making it visually appealing and easy to digest. A lot of our marketing is direct contact and sponsored marketing. Doing email, sending out newsletters, direct contact and working relationships on professional platforms such as LinkedIn, and things like that.

How do you scale, and what is your targeted level of growth? Thats the beautiful thing. What we implemented here as a base is very easy to manage. Scaling is just outward scaling. If you need more scouts or support, its adding those individuals in. I dont think you need a ton, considering your customer base, if you look at it, is third entities in basketball. As you go into other sports, youll get more and more, but really you need to support them from any questions or anything they have. Its easy scaling for our platform that is easy to manage and is pretty self sustainable. For our growth, especially with any first round of funding we get, we want to make sure we expand from the legal side and add more people for marketing, media and, of course, a couple more scouts. If we could double our staff size that would be ideal. That will probably sustain us for quite some time.

Who are your competitors, and what makes you different? Thats another beautiful part because theres not a lot of competitors. I was beating myself up trying to figure out who our competitor is. In this space, you have people who are doing half of what we do. There are independent sport scouting agencies, consulting agreements and things of that nature being hired by teams and assisting in-house scouts. From a technology perspective, we have a lot of companies doing video analysis and things like that, like SportVu, AutoStats, NetScouts. None are doing video scouting and analysis and producing results. They are doing two of those at max. Thats what sets us apart, were giving you a holistic solution that is plug and play as well because we have what we value and what we think, but the way the model works is say you have anything you feel should be weighed more than others. We can easily tune that to your needs based on your understanding. Say the organization has their own proprietary metrics or something like that. As long as it's data or something that can be quantified, we can plug that in and factor it in. That separates us from everyone else.

Whats the unfair advantage that separates your company? Its the proprietary aspect of it. We figured out some metrics from a scouting perspective, the visual scouting, being able to generate those metrics, and putting those into our proprietary algorithm. Also, one of our proprietary metrics weve created based off of research and historical context is potential. The potential rating is based on what prospects are capable of doing and some other historical analysis, which when added up produces their potential score. Its our proprietary statistics and analysis that is the unfair advantage we have.

Baseline Scouting

What milestone have you recently hit or will soon hit? Were doing a lot of network building. It has expanded very much. Were connected with a bunch of the teams, if not all of the teams in some aspect at this point. Through our partnership network, were starting to connect with other companies. Weve had talks with other companies on that. Thats the biggest milestone, actually having a network that our name is in and starting to get well known. With our newsletters and things we are sending out, were starting to get the ears and the eyes of some of these organizations. The final thing before we really start making money is to get in the rooms and start having these conversations with these teams and I think were on the cusp of that with our latest information shares and demonstrations.

What are the values that are core to your brand? Our core value is and I learned this from other companies is a family-oriented type of company. A lot of people that work with us are through previous relationships, actual familiar relationships and once you bring in someone like our partner Zinn Sports Group and they have someone that is reputable and they bring them in, its all something that is very self-built and close-knit. Because of that, we work very well together. Ive seen that in other workplaces. You want to keep and maintain it. I would also say simplicity. A lot of things with technology that deal with AI and machine learning and you start getting in-depth with that, you start to lose the simplicity of everything. Things start to get convoluted and you start to lose the goal.

What does success ultimately look like for your company? Success for us would be one of two things. Being the go-to product for all teams in any given league we sell it in, thats the ultimate success to me. When youre in a league, competition is king and money talks. If someone were ultimately to buy it out and absorb us into an organization and we have a role there, that would be a success as well. Ultimately, it would be optioning, which is to be in-house for all the teams in the league.

What should investors or customers know about you the person, your life experiences that shows they can believe in you? Its being knowledgeable in both areas of what we are selling. From an IT perspective, Ive been in the cybersecurity world for more than a decade. Ive done customer service. So not only do I know how to walk the talk, I know how to talk the talk and make sure were actually solving problems and putting out a product that does something when you say it and ultimately assist with the goal of making someone successful. From my roots, I do very much value what being an inner-city New York kid, and really inner-city anywhere, but I can speak very fondly of New York and the experience of what it means to be somewhere gritty and understand when you make it out of somewhere and have those values, you can do anything you set your mind to. My goal is to provide you with service and product thats going to do right by you, and make sure you know we are going to work closely to help us grow and the company grow as well.

What makes your analytics and scouting better than the competitors? One thing is the base of what we do. A lot of people have these technologies and means for the pro game. When it comes to the amateur level and outside of the pro game, there is a big gap in what people are doing, so that takes time, resources and money. We have that out there. Were already doing that and figured out that simplistic way to do it and weve boiled down whats meaningful. When I talk about whats meaningful, people will scout every game and be like, He dropped 30 points in this game. Im like, Well, that doesnt mean anything because its not translatable. Anything weve done, weve done the research, worked through our algorithms, means and methods to say were doing meaningful, translatable actions as our goal. Thats going to set us apart because weve already done that work and gone through that and a lot of these others dont care to go that deep into this level or are looking at something very niche or narrow like player movement. Thats great from understanding by a biomechanical aspect and stuff like that, but thats not getting into the game that shifts so rapidly. Our platform is dynamic and able to adapt with that stuff.

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Baseline Scouting's B2B system for teams combines the eye test ... - Sports Business Journal