Latest news with #analytics
United News of India
2 hours ago
- Business
- United News of India
OPGC conferred with National ET PSU Silver Award for Digital Transformation
Bhubaneswar, June 21 (UNI) Odisha Power Generation Corporation Ltd. (OPGC) has been conferred with the prestigious National ET PSU Silver Award under the category Leadership in Digital Transformation. The award was presented at the ET PSU Leadership & Excellence Awards 2025, held in New Delhi on June 19, OPGC sources said here on Saturday. The conference brought together the nation's leading public sector enterprises, with OPGC emerging as a beacon of innovation and operational excellence. Competing against more than 50 of India's most prominent PSUs, OPGC's pioneering digital initiatives set it apart as a trailblazer in the power generation sector. The jury lauded OPGC for its transformative use of OSI PI Historian, real-time analytics platforms, and advanced process automation—solutions that have redefined plant operations and significantly enhanced efficiency. The implementation of these cutting-edge technologies has not only streamlined OPGC's operations but also established new benchmarks in the industry. By harnessing the power of real-time data and automation, OPGC has demonstrated how digital innovation can drive sustainable growth and operational resilience. The award was received on behalf of OPGC by GM (Operations) Sudhakar Swain and Manager (Engineering & Efficiency) Prabhupada Acharya. OPGC Managing Director K. R. Pandu, Director (Operations) A. R. Dash, and Senior VP (Operations) Sukant Mohapatra extended their heartfelt congratulations to the entire OPGC team for their unwavering dedication and innovative spirit. 'This national recognition further cements OPGC's commitment to leveraging digital technologies for sustainable progress,' they said. UNI DP ARN
New York Times
4 hours ago
- Sport
- New York Times
Off-target shots: How the rise of the ‘good miss' is aiding talent ID in football
Off-target shots are football's missed opportunity, in more ways than one. As the analytics revolution began to sweep through the sport in the early 2010s, shots on goal quickly became a key area of interest, with the aim of identifying the game's most ruthless finishers firmly in mind. Some years after expected goals (xG) came expected goals on target (xGOT) — a metric that estimates the quality of on-target shots, taking into account factors such as the angle from which the shot was taken from, and its placement within the goal frame, to give an indication of how likely the subsequent effort was likely to find its way in. Advertisement It has since been used to evaluate both finishing ability — rewarding players who turn low-quality opportunities into high-quality attempts on goal with a positive shooting goals added (SGA) score — as well as goalkeeping, helping to distinguish those who consistently keep out the toughest-to-save shots. Given that xGOT works on the premise of scoring probability, however, it swiftly disregards shots that statistically have no chance of going in. And so for years, conventional shooting models would penalise a horrible miscue that flies high, wide and out of the stadium, just as harshly as it would a curling, dipping shot that just grazes the woodwork from the same spot; with a flat score of zero. This is traditionally where the eye test comes in; you would instinctively prefer Kevin De Bruyne to line up your long-range free kick than the centre-back who keeps putting speculative efforts into touch. But given that over half of non-blocked shots were off-target in the Premier League last season — that's more than 3,500 — there is surely extra value to be found. Are some players 'better' at missing than others? And can answering that help to uncover some wayward finishers who could soon be about to click into a more clinical gear? There is, of course, a simple way of measuring how often players go close with their shots, and that's by checking how often they hit the woodwork. Filtering from the start of the 2022-23 season, across Europe's top four leagues, and some likely names emerge; Robert Lewandowski is the player to strike the post or crossbar most often, with 14 across the last three seasons, while Darwin Nunez follows closely behind on 11. Both make sense, given how often they get into close-range goalscoring positions. Looking at that same statistic as a proportion of the player's total shots is more revealing, with Villarreal's Yeremy Pino leading the way. He is underperforming his expected goals output by just over six goals since August 2022, but has hit the woodwork six times, suggesting he might be more unfortunate in front of goal than a quick glance at his shooting statistics suggest. Advertisement Ten of the 15 players listed below perform below expectation on the SGA model, all punished by multiple scores of 0.0 xGOT for their woodwork shots, but our viewing experience tells us that players such as Youri Tielemans, Harry Wilson and Matias Soule can strike a ball well — an encouraging start to our search for finishers that slip through the statistical net. How about inflating the margin for error further, to capture those shots that whistle just wide? By expanding the size of the goal by 25 per cent, as illustrated below, we can see which players routinely go close with their efforts on goal, even if they don't clip the woodwork on the way out. Again, it's the high-volume shooters that rise to the top — Erling Haaland (23), Lautaro Martinez (23), and Lewandowski (22) lead the way for the sheer quantity of near misses across the sample — but taking a proportional approach reveals an interesting mix of players. The bar chart below suggests that high-calibre finishers such as Son Heung-min and Jamal Musiala — both comfortably scoring more than the quality of their chances suggest that they should across the sample — weren't far away from posting even more clinical numbers. But it also picks out a handful of players who look to be struggling according to xG data in isolation. Tottenham's Brennan Johnson, for example, had six near misses to his name in 2024-25 alone, while Samuel Lino, who scored only 13 non-penalty goals in La Liga from an expected value of 19.3, saw just over a quarter of his shots graze past the post, considerations that should surely be taken into account, even when evaluating shooting ability on a macro level. What all of the above fails to take into account is the quality of opportunity in the first place. If one player hits the post from point-blank range, while another crashes his shot off the crossbar from distance, it would skew the data to label one just as 'unfortunate' as the other. To remedy that, we can combine pre-shot xG data to our sample, to see which players are regularly turning tricky opportunities into close misses. Advertisement Doing so creates an xGOT-style metric for off-target shots, rewarding players who consistently almost hit the target from low-probability shooting zones. Of players to have missed at least 50 shots across the last three seasons, we have calculated the average distance by which their efforts have missed the goal, converting this to a score between zero and one using a logarithmic scale. Those who combine close misses with low xG shots, are the players who add most 'value' to their off-target efforts on goal. The results pass the eye test, with some familiar names cropping up from our previous searches. Matias Soule makes his third bar-chart appearance, with the evidence stacking up that 22-year-old is an accurate shooter who should take aim more often. Jorgen Strand Larsen just misses the cut to make it three out of three as well — no Premier League player hit the target with a higher proportion of their shots across Europe's top five leagues last season (61.1 per cent) — and this experiment suggests that even when does miss, he tends to put it just past the post. But this new list of players seems to reward talented ball-strikers, players such as Jarrod Bowen, Andreas Pereira, Hakan Calhanoglu, as well as long-range specialists Kevin De Bruyne and Bruno Fernandes, who consistently back themselves to get through the ball with power and precision from range. Some case studies stand out. While Raphinha is enjoying his most prolific season in front of goal, his league xG statistics still show underperformance. Similarly, Lamine Yamal has only ever scored below expectation in La Liga, despite his obvious ability to cut inside and curl the ball accurately towards the far corner. Once again, it suggests that off-target shots deserve more consideration in conventional analysis of a player's shooting skill, and can serve as a predictor for eventual improvement in the future if the same pattern of near-missing continues. Work continues at club level on how to use off-target shots in conjunction with traditional xGOT to get a broader picture of finishing ability. A data scientist at Nottingham Forest, Alex Marin Felices, delivered a talk at the inaugural Field of Play conference in Manchester in March, referring to research that shows a higher correlation between future player performance and metrics that combine on and off-target shots. Advertisement He also referred to progress on adapting xG models to specific positions and players, as well as the tracking of blocked shots, to continue the improvement of the model. Helping clubs identify skilled finishers earlier can allow them to acquire higher-quality players quicker — and more economically — crucial for those looking to get ahead of the recruitment curve. For our purposes, it's another reminder that underlying shooting metrics, while an invaluable tool over long periods of time, can still be enhanced with context. Raphinha, Soule, Lino, and Strand Larsen; keep shooting.
Entrepreneur
14 hours ago
- General
- Entrepreneur
"მეტროპოლ შინდისი" - სახლი, რომელიც ბუნების სიახლოვეს ჩაიფიქრე
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Fast Company
19 hours ago
- Business
- Fast Company
The AI era is here. Are you ready to lead?
Artificial intelligence (AI) is no longer a fringe experiment or a future-facing buzzword. It's a here-and-now force that's rapidly reshaping the modern business landscape. AI is already transforming how organizations operate—from how they close their books to how they engage with customers—and the pace is only accelerating. Are we ready for it? For too many leadership teams, the honest answer remains: not yet. AI's potential to improve back-office efficiency is well documented, particularly in finance functions that are often stretched thin. Intelligent automation can significantly reduce the time spent on tasks like transaction matching, reconciliations, and journal entries, freeing up teams to focus on higher-value activities like forecasting and strategic planning. Companies implementing intelligent process automation are seeing measurable impact in areas such as cash flow forecasting, anomaly detection, and real-time variance analysis. By drawing on clean, well-structured data, AI can surface patterns that eliminate risk and unlock opportunity, turning raw numbers into actionable insights. This evolution comes at a crucial time. Many finance departments are contending with a shrinking workforce and increasing expectations. Nearly 300,000 accountants and auditors have left the profession in the last two years, and the majority of CFOs now say they are responsible for enterprise-wide data and analytics. AI can help fill the gap if implemented thoughtfully. WHY RUSHING CAN LEAD TO FAILURE Obvious benefits aside, AI is not a silver bullet. Organizations that rush implementation without the right foundations risk doing more harm than good. Inaccurate data, poor governance, and a lack of human oversight can lead to flawed outputs, audit issues, and regulatory exposure. AI models trained on inconsistent or incomplete data can replicate human mistakes at scale, producing errors with more speed, not more accuracy. That's why clean, auditable data must be a non-negotiable starting point. Gartner predicted that 30% of generative AI projects will be abandoned after proof of concept by the end of 2025, in large part due to poor quality data. In other words: garbage in, garbage out. GOVERNANCE BEFORE GROWTH Before deploying AI at scale, leaders must build the proper guardrails. That starts with a robust data governance strategy that defines how data is collected, maintained, and used across the organization. Assigning data stewardship responsibilities, establishing quality standards, and ensuring alignment with regulatory frameworks are essential. Just as important is keeping human judgment in the loop. Especially in the Office of Finance, where compliance and auditability are paramount, AI tools should be explainable and traceable. The goal is not to replace decision makers, but to empower them with better information. Cross-functional collaboration between finance, IT, compliance, and legal teams is critical to achieving this balance. AI readiness is about people as much as it is about technology. Implementing AI effectively means helping employees evolve alongside it. That includes upskilling employees to become data-literate advisors, building comfort with new tools, and fostering a culture of experimentation balanced by accountability. Many of the skills teams already possess, such as data analysis, pattern recognition, and variance identification, map directly to AI-adjacent competencies. With the proper training and support, these professionals can become critical players in an AI-enabled function. LEADERS MUST GUIDE AI STRATEGY As AI becomes foundational to business success, it's no longer sufficient for executives to delegate its implementation to IT. CEOs and their leadership teams must play an active role in shaping AI strategy, from identifying high-impact use cases to defining clear KPIs. That includes assessing where AI can truly improve and where human judgment remains irreplaceable. It also means investing in the right tools—not necessarily the most complex or expensive ones, but those that simplify workflows and integrate well with existing systems. Ultimately, responsible AI leadership is about setting direction, removing friction, and creating a culture where teams feel both empowered and accountable. BUILD NOW OR SCRAMBLE LATER There's no doubt that AI will reshape how businesses operate. The only question is whether your organization will lead the way or struggle to catch up. A recent Deloitte survey of the financial services industry, an industry that typically is slow to adopt new technology, found that AI 'pioneers' or early adopters, see significantly more value from their AI deployments than 'followers' or those who are slow to bring the technology into their organization. Companies that act now by improving data quality, establishing governance, investing in their people, and piloting AI in high-impact areas will gain a durable competitive advantage. AI is not just a tool for transformation—it's a test of readiness. And in today's rapidly evolving landscape, readiness isn't optional—it's everything.
Entrepreneur
19 hours ago
- General
- Entrepreneur
Honor 400 Series, 200 მეგაპიქსელიანი ულტრა-მკაფიო AI კამერით უკვე საქართველოშია
This website utilizes technologies such as cookies to enable essential site functionality, as well as for analytics, personalization, and targeted advertising. To learn more, view the following link:
