Data Archives - Tricky Enough

How Image and Video Annotation are Helping Achieve Precision in Sports?

SunTec .AI — Tue, 22 Jul 2025 10:50:18 +0000

Have you ever thought about how sports analysts predict match outcomes? They use techniques from players’ past games to make their predictions. Sometimes, the analysts also determine what kind of shot a player is going to serve in upcoming matches. These predictions are possible due to the availability of image and video annotation.

According to a Dimension Market Research report, the data annotation market size is expected to grow at a CAGR of 34.4% by 2033. This reflects the increasing importance of data annotation in various domains, including healthcare and even sports. Here, data annotation plays an important role in training AI models for conducting sports analytics.

The analysis process involves using data science and statistical techniques to gain meaningful insights regarding the performance of athletes and sports organizations. To generate better analytics results, leveraging AI can be a suitable approach.

Along with real-time analysis, AI aids in proper rule enforcement to ensure a fair game by providing reliable insights regarding false penalties. As a player, you can achieve a good physique by opting for AI-driven customised training programs. AI can also monitor sleep patterns and fatigue levels to determine the possibility of any injuries during the game or beforehand.

Let’s learn about image and video annotation in sports analysis and how a well-annotated sports dataset offers informative insights.

Importance of Data Annotation in Sports

Over the years, the usage of data annotation has increased tremendously in sports. While training AI models to analyse sports data, annotation becomes an important process. It facilitates the labelling of data to identify entities such as equipment, players, and other match officials. Such usage of data annotation in sports provides the following benefits:

Better Decision-making: Video and image annotation services facilitate motion tracking in sports. With the help of these annotation solutions, you can monitor players’ actions to make well-informed on-field gaming decisions.
Improving Player Performances: Data annotation assists athletes in enhancing their performances. Using the visuals provided by annotation software, players can observe positive and negative aspects of their playing techniques.
Personalising Content: By annotating game footage, broadcasters can stream detailed and exclusive content so that viewers and sports analysts can gain precise players’ performance data.

Key Data Annotation Techniques Used in Sports

Depending on the data type, there are different types of annotations, such as text, image, or video. Some techniques to annotate these data types are:

Keypoint Annotation

The keypoint annotation technique aids in labelling some specific points or features of objects within images or videos. You can label multiple points and use them to train AI models in such a way that they can form complete structures by connecting the labelled data points. This enables the models to identify the orientation, position, or movements of objects.

You can leverage keypoint annotation for motion tracking in sports such as football, basketball, or badminton.

Bounding Box Annotation

Bounding box is a technique of creating a rectangular outline around objects while detecting the positions and dimensions of an object. Every box is allotted a class label to simplify object tracking for AI models. While annotating sports data, bounding box annotation helps in labelling of gaming equipment, players, and referees.

3D Annotation

3D annotation involves labelling and classification of objects in three-dimensional space. It helps in understanding the depth and volume of an entity in addition to its position and orientation. 3D annotation is especially used when an image or video consists of point clouds. These are sets of data points in 3D coordinate systems and represent the geometry of an object.

Bounding box, LiDAR, and photogrammetry are some of the common approaches for 3D annotation. In sports, you can utilise 3D annotation to detect the trajectory of a ball in games such as cricket, football, or basketball.

Semantic Segmentation

It is a special type of data annotation technique that involves labeling pixels within images or video footage. Semantic segmentation is used during sports analysis for annotating images of field boundaries in a game or special zones such as penalty areas in football.

How are Image and Video Annotation Techniques Strengthening Sports?

Analysing Game in Real-time

As a coach, using image and video annotation platforms for game analysis helps monitor players’ performance, including their positions and movements. Based on this information, you can refine the game strategy to increase the probability of winning.

For example, in cricket, video annotation services help identify which batsmen perform well against a specific bowling style. Based on this, reshuffling the order in which the players are sent for batting becomes easy.

Building High-Impact Teams

Performance analysis using video annotation services helps in creating separate groups of high-performing and low-performing players. You can include the best-playing candidates on the team for subsequent matches and send underperforming players for training.

This decreases the pressure on players who are not performing well and provides them time to enhance their playing skills.

Injury Prevention

Data annotation techniques help in labelling patterns in player movements that can cause injury. By detecting these activities, players can cautiously avoid such movements to prevent injury.

The Hurdles of Annotating Sports Data

Annotation Errors

Inconsistencies in data annotation guidelines can result in discrepancies in labelling data. This leads to the creation of a flawed training dataset. AI models trained on such datasets give inaccurate outcomes.

Complexities of High-Volume Data

Image and video annotation for sports analysis involves labelling large volumes of data. This process is challenging and time-consuming. Further, as the amount of data increases, annotation becomes complex due to a lack of clarity or similarity in data records.

Annotation of Complex Movements

Any sport usually involves fast and spontaneous actions, such as throwing or running, which are difficult to annotate. In addition, every player has a different biomechanical profile, due to which their actions can differ from each other. This makes it challenging to use uniform annotation techniques for labelling the activities of all the players in a game.

Best Practices for Using Data Annotation in Sports

Define Clear Annotation Guidelines

There should be clear guidelines to annotate datasets. To do this, first thoroughly understand the types, formats, and attributes of data records. To frame proper annotation guidelines, you can take assistance from AI. Alternatively, it is more suitable to adopt a hybrid approach in which AI can perform initial labelling, which can then be rectified by a team of reviewers.

Refine Your Data Annotation Strategy

Prepare a robust data annotation strategy specifically for the sport that you want to analyse. Even after completion of labelling, regularly monitor the efficiency of the annotation process and make adjustments if required.

Minimize Rework

Invest in the right data annotation tools, and hire an expert team to save the cost of rework. This improves the annotation process and accuracy of analytical insights.

Thus, by facilitating the labelling of players’ positions and orientations of players, coaches, and game equipment, annotation techniques have transformed the world of sports. The AI models trained on annotated data are helping sports experts convert raw information into actionable insights. In the future, you will witness further enhancements in the use of AI for the sports domain.

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The post How Image and Video Annotation are Helping Achieve Precision in Sports? appeared first on Tricky Enough.

Prove Performance in Real Time: Stop Defending Delivery Without Work Data

Amanat Kaur — Mon, 14 Jul 2025 23:54:39 +0000

When your delivery is solid but the client’s still not convinced, you don’t have a performance issue but a proof gap. That’s what happens when expectations rise faster than visibility. Tasks are completed, timelines are met, but confidence wobbles because the client can’t see how your team got there.

This article will show how to shift from defensive explanations to visible proof. Remote work tracking software allows you to show precisely where time goes, what’s getting done, and how your team stays accountable in real time.

Where Performance Proof Breaks Down First

Even high-performing remote teams hit friction when visibility doesn’t keep up with delivery. You’re not just trying to finish the work. You’re trying to show it was done efficiently, responsibly, and with smart use of time.

Here is where the cracks form:

Delivery Updates Lack Context: Clients hear “It’s done,” but don’t know how or why it took the time it did.

Time Logs Don’t Match Client Perception: Your team put in the hours, but the client sees idle time or scope creep.

Ad Hoc Reporting Burns Hours: Building manual reports after the fact drains ops time and delays responses.

Performance Talks Become Defensive: Without clear data, even a strong team ends up explaining instead of leading the conversation.

How to Shift from Delivery to Proof-Driven Trust?

Showing output isn’t enough if the process behind it stays hidden or adds extra admin work to your team.

These strategies give you real-time proof that speaks for itself, backed by data your client can trust:

1. Simplify Time Reporting with Live Visibility

When teams log hours at the end of the day, time looks clean when it’s fragmented. You end up explaining how six hours turned into nine, or why something slipped.

Live time capture fixes that. It automatically records which apps were active, which tags were applied, and how long tasks took. That gives clients real visibility into where time goes, without guesswork.

To make it work, build tagging into the workflow so every hour is tied to a project, task, or category. When you can show that in real time, clients stop asking for proof because it’s already there.

How can remote work monitoring software make project effort easy to explain?

Remote work monitoring software creates continuous logs of project-related activity. Every click, switch, and tag becomes part of a real-time map of effort.

When a client questions why a task took longer than expected, you can show that additional review rounds or mid-task reassignments extended the timeline, with timestamps and context to match.

2. Reframe Delivery Updates Around Team Behaviour

A study found that time spent on email, chat, calls, and meetings has shot up by over 50% in the last decade, and now eats up 85% or more of most workweeks.

Finishing a task on time does not always tell the full story. The process may have been messy with too many meetings, late changes, or constant switching. When clients sense something feels off but cannot see why, they start questioning their performance.

Behaviour-based insights help fill that gap. Instead of only sharing what was delivered, you can show how the team worked, how much time was focused, how tasks moved, and where the workload shifted. That gives clients a fuller picture of the effort.

Include behaviour snapshots in your updates. Focus time versus meeting time, time spent in communication apps, or changes in task ownership all help prove you are actively managing more than just deadlines.

How can a remote employee tracking tool make delivery behaviour easier to explain?

A remote employee tracking tool captures how teams spend time across focused work, meetings, and multitasking.

When midweek delivery slips, the tool shows a sharp increase in time spent in Zoom and messaging apps compared to focused project work, helping you pinpoint where priorities shifted and why progress stalled.

3. Use Proof Points to Get Ahead of Escalations

Waiting until a review call to explain a delay puts you in defence mode. Even if the work is done, trust drops when clients don’t see how or when it happened. Missed SLAs are one risk, but silence is what erodes confidence.

Use work data to flag risks early by surfacing drops in shift coverage, spikes in idle time, or disappearing focus hours. Bring those insights forward before the client even asks to show you’re managing actively, not reacting late.

Set up weekly snapshots or live trend alerts to catch issues in real time. Share them before reviews so you lead with facts and next steps. That shifts the tone from explaining problems to showing how they’re already under control.

How can a distributed team monitoring system help surface risks before escalation?

A distributed team monitoring system highlights early shifts in work behaviour that affect delivery.

When focus time drops and idle time spikes, the tool flags the change and connects it to time spent in non-project apps, so you can step in before SLAs are at risk.

4. Tie Accountability to Real Work, Not Online Status

Clients often confuse being online with being productive. Green dots and message replies create pressure without showing real progress. That kind of visibility leads to assumptions, not insight.

Shift the focus to actual work by tracking time on task, session patterns, and tool use to show how work gets done. This moves the conversation from availability to output.

Use role-based benchmarks to define what good performance looks like. Scorecards and dashboards help flag when effort matches expectations or when it falls short. With real data in hand, accountability becomes shared. Clients see results, not just activity, and your team can align around real performance, not signals.

How can employee monitoring tools reinforce real accountability?

Insightful’s employee monitoring software tracks task-specific activity in real time, giving a direct view into effort across roles.

When a team member finishes three key deliverables but logs fewer active hours than others, the tool highlights their efficiency. That lets you defend results without relying on “hours online” as a stand-in for real work.

5. Back Every Delivery Conversation With Real-Time Work Data

When your work is measurable, your value becomes visible. Real-time data from a monitoring tool lets you show how work happens, not just what gets done, so clients stay aligned without needing constant reassurance.

Here’s how a monitoring tool helps you lead every delivery conversation with confidence:

Live Time Capture: Show where time goes without manual input.

Focus Insights: Explain delivery patterns with context from real behaviours.

Custom Tags: Break down effort by scope and phase for instant proof.

Trend Alerts: Flag productivity shifts early so you stay ahead of client concerns.

Conclusion

Client trust shifts from reactive to consistent when delivery is backed by real-time proof. Monitoring tools anchor that trust by making the effort and output measurable.

As your team’s work becomes easier to see, alignment becomes faster and more consistent.

The post Prove Performance in Real Time: Stop Defending Delivery Without Work Data appeared first on Tricky Enough.

Top 10 Companies With Highest Followers on LinkedIn

Yibeni Tungoe — Tue, 01 Oct 2024 04:38:46 +0000

The top 10 companies with the most followers on LinkedIn are filled with multinational companies that have become name brands in their respective industries. Some of these companies deal with technology, food, media, etc.

LinkedIn is an employment and business-based platform. People use this platform to get jobs and build business connections. All these companies on this list utilise LinkedIn to expand their reach and employ new and bright minds. The following companies listed below chronologically show the top 10 companies with the most followers on LinkedIn.

1. Google

Starting this list of the top 10 companies with the highest number of followers on LinkedIn is Google, one of Alphabet Inc.’s subsidiaries. Besides Google, Alphabet owns other huge companies such as YouTube, Fitbit, and Nest. Google boasts 34 million followers on LinkedIn with more than 300,000 associated members. Google was co-founded by Larry Page and Sergey Brin in 1998. This huge tech company is also involved in search engine tech, e-commerce, online advertising and marketing, artificial intelligence and more. Ruth Port currently serves as Google’s President with Sundar Pichai as the current CEO.
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